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2024 International Conference on Advances in Data Engineering and Intelligent Computing systems, ADICS 2024

作者： Vasanth Kumar, V. Tamizhselvan, C. Manoharan, L. Vishnu Sekhar, R. Vinoth Kumar, V. Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Chennai India School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India School Of Bioengineering Srm Institute Of Science And Technology Integrated Bioprocessing Laboratory Department Of Biotechnology Kattankulathur India

ISBN: (纸本)9798350364828

Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specifications and design, estimating the price beforehand becomes essential to cater to their needs and expectations. Hence, accurately forecasting mobile phone prices is a vital step in the product launch process to remain competitive and assess the market dynamics and competitors. The primary aim of this research is to determine the true market value of mobile phones. In this research, the dataset was extracted by means of utilizing a web scraping process from the flipkart website. The main objective of this research is to fetch the most recent dataset based on the most recent features. The extracted dataset is fitted into regression models such as linear regression, decision tree regressor, random forest regressor, KNN regressor, and gradient boosting regressor and performs the comparative analysis. The models are evaluated based on the mean absolute error metric. © 2024 IEEE.

关键词： Decision trees

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Lung Cancer Diagnosis and Classification Using Hybrid Neural Network Techniques

Lung Cancer Diagnosis and Classification Using Hybrid Neural...

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2023 International Conference on Sustainable communication networks and Application, ICSCNA 2023

作者： Yasmin, M. Andrew Roobert, A. Francis Xavier Engineering College Department of Computer Science and Business Systems Tamil Nadu Tirunelveli India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350313987

Lung cancer remains a formidable global health challenge, with early and accurate diagnosis being a critical determinant of patient outcomes. In response to this pressing need, this research presents a novel approach for lung cancer diagnosis and classification utilizing hybrid neural network techniques. By harnessing the synergistic power of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), we have developed an integrated model capable of effectively processing diverse data sources to enhance diagnostic precision. The first component, CNNs, is employed for feature extraction from medical imaging data, such as X-ray images and computed tomography (CT) scans. These networks excel in capturing intricate patterns and subtle abnormalities in medical images, contributing to the early detection of lung cancer. The second component, RNNs, leverages sequential patient data, including clinical histories, genetic information, and longitudinal records, providing valuable contextual information for comprehensive analysis. The hybrid neural network model facilitates a holistic approach to lung cancer diagnosis and classification, as it unifies the strengths of both data-driven imaging analysis and sequential data interpretation. Experimental evaluations of this approach reveal its potential to achieve superior accuracy, outperforming traditional methods, and thus allowing for timely and precise cancer diagnosis. This advancement holds significant promise in improving the prognosis and management of lung cancer patients, potentially leading to more personalized treatment strategies and ultimately reducing the burden of this devastating disease. As such, this research contributes to the ongoing efforts to enhance lung cancer diagnosis, making strides towards improved patient care and survival rates. © 2023 IEEE.

关键词： computed tomography Scan Images Convolution Neural networks Lung Cancer Recurrent Neural networks X-ray Images

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Deep learning and encryption algorithms based model for enhancing biometric security for artificial intelligence era

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Journal of Ambient Intelligence and Humanized Computing 2024年 1-14页

作者： Byeon, Haewon Shabaz, Mohammad Surbakti, Herison Keshta, Ismail Soni, Mukesh Bhatnagar, Vaibhav Department of Digital Anti-Aging Healthcare Inje University Gimhae50834 Korea Republic of Model Institute of Engineering and Technology J&K Jammu India Information and Communication Technology Rangsit University International College Pathumthani Lak Hok Thailand Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of CSE University Centre for Research & ampDevelopment Chandigarh University Punjab Mohali140413 India Department of Computer Applications Manipal University Jaipur Jaipur India

The significance of facial recognition in the era of artificial intelligence lies in its utilization of facial features as a type of biometric characteristic possessing uniqueness and irreversibility. However, exposing these features to attacks, tampering, or unauthorized disclosure poses considerable threats to user privacy and security. A privacy and security solution based on deep learning and encryption algorithms is proposed to tackle this issue. This solution employs the FaceNet deep learning algorithm to extract facial features efficiently. The combination of biometric feature blurriness and cryptographic system precision is achieved, utilizing the CKKS fully homomorphic encryption algorithm for operations in the ciphertext domain of facial recognition. The SM4 algorithm is used to enhance the resilience of facial feature ciphertext against malicious attacks. By leveraging the properties of symmetric ciphers, a balance is achieved between security and computational efficiency. The management of the symmetric key used in the SM4 algorithm is conducted through the employment of the SM9 asymmetric encryption algorithm. Experimental results and analysis demonstrate that the proposed solution enhances the security of data transmission, storage, and comparison without compromising the accuracy and efficiency of facial recognition. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Differential privacy

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Evaluation of Cloud Recovery System for Financial Applications Based on Hadoop 7

Evaluation of Cloud Recovery System for Financial Applicatio...

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7th IEEE International Symposium on Telecommunication Technologies, ISTT 2024

作者： Lu, Li Lu, Tong Sali, Aduwati School of Microelectronics and Data Science Anhui University of Technology Maanshan243002 China Department of Computer and Communication Systems Engineering Faculty of Engineering Selangor Serdang43400 Malaysia Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350392074

The rise of cloud-based disaster recovery systems has reshaped disaster recovery practices. Integrated into the Hadoop framework with core components like Hadoop Distributed File System (HDFS), MapReduce, and Yet Another Resource Negotiator (YARN), these systems ensure data integrity and business continuity during crises while enhancing data reliability and operational efficiency, surpassing traditional recovery systems. This study presents a comprehensive comparative analysis of traditional and Hadoop-integrated cloud recovery systems, focusing on hardware architecture and performance. Architecturally, it highlights the stark differences between conventional models and Hadoop-based innovations. The results show that the Hadoop-backed recovery system exhibits remarkable improvement, reducing Recovery Time Objective (RTO) from over 18 seconds to under 7 seconds while preserving data integrity as per Recovery Point Objective (RPO) standards. This finding underscores the academic importance of evaluating cloud-based disaster recovery within the Hadoop context, offering insights into its transformative potential for disaster recovery strategies. © 2024 IEEE.

关键词： Data integrity

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Three-Step Carrier Frequency Offset Estimation Algorithm for Burst-Mode communications*

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Chinese Journal of Electronics 2023年第1期19卷 165-169页

作者： Wu Luo An Liu Qinglin Liang State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronic Engineering and Computer Science Peking University Beijing China

A data-aided frequency offset estimation algorithm suited for burst-mode communications is described and analyzed. The proposed algorithm is based on a Pilot symbol assisted modulation (PSAM) frame structure, which consists of regularly spaced pilot symbols and a group of consecutive pilot symbols embedded in. Firstly, an initial estimate based on consecutive pilot symbols is generated for reducing the phase ambiguity. Secondly, based on both spaced and consecutive pilot symbols, fine preliminary estimates are generated to improve estimation accuracy. Lastly, the final estimate is obtained by combining the fine preliminary estimates with the initial. This three-step algorithm is shown to provide high estimation accuracy close to those of FFT-based algorithm at low Signal-to-noise ratio (SNR). In addition, it is suitable for high carrier frequency offset cases.

关键词： Frequency modulation Simulation Symbols Estimation Frequency estimation Signal to noise ratio

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Channel Estimation for 29GHz mm Wave MIMO systems in Tropical Region 7

Channel Estimation for 29GHz mm Wave MIMO Systems in Tropica...

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7th IEEE International Symposium on Telecommunication Technologies, ISTT 2024

作者： Lu, Li Sali, Aduwati School of Microelectronics and Data Science Anhui University of Technology Maanshan243002 China Faculty of Engineering Department of Computer and Communication Systems Engineering Selangor Serdang43400 Malaysia Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350392074

This paper focuses on the 29GHz mmWave MIMO communication system in tropical environments, considering the impact of rain attenuation and providing a detailed analysis of channel estimation and system performance. Experimental results indicate that as the signal-to-noise ratio (SNR) increases, the mean square error (MSE) of channel estimation decreases. Under the same SNR conditions, MIMO systems with different antenna configurations exhibit distinct channel estimation error characteristics. Notably, in the 128x128 antenna configuration, the channel estimation error is most pronounced. Furthermore, increasing the number of training signals significantly reduces channel estimation errors, especially in the 128x128 antenna configuration. This study offers a performance evaluation of the 29GHz mmWave MIMO communication system in tropical regions, providing valuable insights for further research and optimization in this field. © 2024 IEEE.

关键词： Signal to noise ratio

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Shielding Web Application against Cyber-Attacks using SIEM 13

Shielding Web Application against Cyber-Attacks using SIEM

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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Yushko, Andriy Shevchuk, Ruslan Lopacinski, Karol Leszczynska, Maja Yashchyk, Oleksandr Yurchyshyn, Tetiana West Ukrainian National University Department of Computer Science Ternopil Ukraine University of Bielsko-Biala Department of Computer Science and Automatics Bielsko-Biala Poland Wroclaw University of Economics and Business Department of Information Systems Wroclaw Poland Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Computer Technologies Ternopil Ukraine West Ukrainian National University Information and Communication Technologies Department of Foreign Languages Ternopil Ukraine

This paper considers SIEM technology as one of the mechanisms for protecting web applications from cyberattacks. The most common vulnerabilities and attack surfaces on web applications are analyzed and a list of security events that SIEM can handle is provided. Based on the results of the analysis, a systematic approach to the classification and grouping of events, vulnerabilities, and attack surfaces, taking into account their interrelationship, is proposed. The results of the research will be useful for developers, administrators and security analysts who work with SIEM systems, as they will help them make informed decisions about Web Application protection and prompt response to incidents. © 2023 IEEE.

关键词： Network security

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Combining Self-labeling with Selective Sampling

arXiv

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arXiv 2023年

作者： Kozal, Jedrzej Wozniak, Michal Department of Systems and Computer Networks Wroclaw University of Science and Technology Wroclaw Poland

Since data is the fuel that drives machine learning models, and access to labeled data is generally expensive, semi-supervised methods are constantly popular. They enable the acquisition of large datasets without the need for too many expert labels. This work combines self-labeling techniques with active learning in a selective sampling scenario. We propose a new method that builds an ensemble classifier. Based on an evaluation of the inconsistency of the decisions of the individual base classifiers for a given observation, a decision is made on whether to request a new label or use the self-labeling. In preliminary studies, we show that naïve application of self-labeling can harm performance by introducing bias towards selected classes and consequently lead to skewed class distribution. Hence, we also propose mechanisms to reduce this phenomenon. Experimental evaluation shows that the proposed method matches current selective sampling methods or achieves better results. © 2023, CC BY.

关键词： Supervised learning

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Combining Random Forest and Linear Regression to Improve Network Traffic Prediction

Combining Random Forest and Linear Regression to Improve Net...

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International Conference on Transparent Optical networks

作者： Błażej Ułanowicz Dawid Dopart Aleksandra Knapińska Piotr Lechowicz Krzysztof Walkowiak Department of Systems and Computer Networks Wrocław University of Science and Technology Poland

The constant increase in traffic volume triggers the development of new network optimization methods. Machine learning techniques are one of the promising research directions, as they provide the necessary tools to improve network performance in various aspects. Furthermore, prediction-based algorithms are getting the attention of researchers to achieve more precise network operations. In this paper, we approach the traffic prediction problem in backbone optical networks. We propose a model based on the Voting Regressor, combining Random Forest and Linear Regression. We create two large semi-synthetic datasets with daily patterns of various popular network-based services and applications. Through broad numerical experiments, we show how the proposed approach outperforms the reference ones in different traffic profiles.

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Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0

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computers, Materials & Continua 2023年第3期74卷 5349-5367页

作者： Khalid A.Alissa Mohammed Maray Areej A.Malibari Sana Alazwari Hamed Alqahtani Mohamed K.Nour Marwa Obbaya Mohamed A.Shamseldin Mesfer Al Duhayyim SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human *** the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital *** current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 *** suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image ***,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based *** proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover *** secret images,encrypted by signcryption technique,are embedded into cover ***,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax *** integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV *** proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct *** simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

关键词： Unmanned Aerial Vehicles Artificial Intelligence emerging technologies Deep Learning Industry 4.0 image steganography

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