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检索条件"任意字段=International Conference on Information Technology - Coding and Computing"

共 195356 条记录，以下是421-430 订阅

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Enhancing Cancer Classification in Genomic Data: A Hybrid Approach Using Principal Component Analysis and Machine Learning Techniques 3

Enhancing Cancer Classification in Genomic Data: A Hybrid Ap...

引用

3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Periya Nayaki, A. Harshana Shruthi, P. Bavithra, B. Jeevasri, P.R. Thanabal, M.S. Velammal College of Engineering and Technology Department of Information Technology Madurai India Psna College of Engineering and Technology Department of Cse Dindigul India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Using the abundance of genetic data available, cancer categorization using genomic data is an important field of study aimed at improving the diagnosis, prognosis, and treatment of different forms of cancer. Genomic data offers a full picture of the molecular mechanisms behind cancer. This data includes gene expression levels, DNA methylation patterns, copy number variations, and mutations. However, reliable cancer classification is severely hampered by the enormous dimensionality and complexity of these datasets. It is suggested that in order to improve the performance and interpretability of the classification model in cancer data, this hybrid technique should make use of both SVM and other traditional PCA. In order to improve performance while maintaining biological relevance in genomic characteristics, the approach entails employing PCA to reduce the dimensions. After that, the reduced dataset's complicated patterns are found using the SVM model. The strategy is put to the test to see how well it handles high-dimensional genetic data and what potential it creates for improving cancer detection and customized treatment regimens. © 2025 IEEE.

关键词： Lung cancer

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Hybridization of DNA computing and Chaotic System for Ensuring the Randomness and Integrity of Image Data 4

Hybridization of DNA Computing and Chaotic System for Ensuri...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Dhakal, Madhav Shakya, Subarna Tribhuvan University Central Department of Computer Sci. & Information Technology Kathmandu Nepal Pulchowk Campus Tribhuvan University Department of Electronics and Computer Engineering Institute of Engineering Kathmandu Nepal

ISBN: (纸本)9798331523923

In this paper, the concept of DNA computing and 3-D chaotic system is implemented to maintain the security of grayscale image while transmitting through the publicly available network. Here, random selection technique for sequencing of DNA bases with secret key generated through chaotic sequence encryption technique is purposed. Generation of permutation box for diffusion of image pixels carried out on the basis of chaotic system, which create more randomness and difficult to retrieve the secret key and maintain confidentiality from the intruder. System parameter and initial values of chaotic system work as the private key. In this proposed concept, first, plain image is divided in to various block of 512 bit size and padding is necessary if the last block does not contain the block of 512 sizes and then data obtained from the blocks are XOR with the key value obtained through chaotic system. After that, results obtained from this operation are transformed in to the cipher image with using the concept of Central Dogma of Molecular Biology. Experimental result was thoroughly examined using various techniques including histogram analysis, chi-square test, information entropy assessment, differential attack and the result shows that the proposed concept is secure and maintain the randomness, confidentiality and integrity of image data. © 2025 IEEE.

关键词： Gene encoding

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Inside and Inside: Efficient Anomaly Detection by Fully Capturing the Detailed Dynamics

Inside and Inside: Efficient Anomaly Detection by Fully Capt...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Tang, Ziyu Zhou, Xiren Chen, Ao Liu, Shikang Wei, Chuyang Chen, Huanhuan School of Computer Science and Technology University of Science and Technology of China Hefei China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei China

ISBN: (纸本)9798350368741

Anomaly detection in sequential signals is gaining prominence, especially with limited training data and timeliness requirements. Fully extracting the data-inside changing information, we propose a novel Wavelet-Enhanced Reservoir computing framework (WE-Res). Our framework uses Discrete Wavelet Transform (DWT) to decompose signals for multi-level detail and trend extraction recursively. Each level is fitted using an Echo State Network (ESN) respectively, extracting its inside dynamic features into fitted models. Integrating these dynamic features creates a "multi-level dynamic feature" that enhances signal representation, aiding in distinguishing between normal and anomalous patterns. We further introduce a dual-objective optimization to refine ESNs' reservoirs, increasing the fitting accuracy and improving category discrimination among the captured features. Validation on real-world data confirms our method's effectiveness, especially in data-limited scenarios and less training time compared to recent baselines. © 2025 IEEE.

关键词： Anomaly detection Dynamic Feature Model-Based Methods Reservoir computing Time-series signal

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Evaluation of Machine Learning Approaches for Cirrhosis Survival Prediction 3

Evaluation of Machine Learning Approaches for Cirrhosis Surv...

引用

3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Rengaraj, Nivedha Vamsi, Katragunta Mahanth Vijayaraghavan, Gagandeep Srm Institute of Science and Technology Department of Information Technology Chennai India Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331515911

Cirrhosis is a fatal condition characterized by irreversible scarring and loss of liver function. Prediction of cirrhosis treatment outcomes at an early and precise manner is difficult be- cause of issues like data imbalance, feature dimensionality, noises and outliers prevalent in clinical dataset. This study examines the use of supervised machine learning approaches to support this prediction in cirrhosis patients. We analyze the performance of seven predictive models, such as Decision Trees, Random Forests, Gradient Boosting Machines (GBMs), K-Nearest Neighbors, Extra Trees, and Logistic Regression based on their accuracy, F-1 score, Recall, Precision, and Matthews Correlation Coefficient (MCC). Outlier detection using isolation forests is used to refine data quality, while SMOTE resampling addresses class imbalance and a Genetic Algorithm optimizes feature selection for improved model performance. LightGBM and XGBoost proved to be the most successful methods with an AUC of 0.94. The combination of XG- Boost with Genetic Algorithms demonstrates superior predictive power, marking a novel methodology for automating liver disease diagnosis. The results show that the combination of the XGBoost algorithm and Genetic Algorithms is best suited for automating liver disease diagnosis. This methodology promotes early diagnosis and intervention based on improvements in the efficiency and precision of predictions. © 2025 IEEE.

关键词： CatBoost Extra Trees KNN LightGBM Liver Cirrhosis Health Logistic Regression Histogram Gradient Boosting Machine Learning Random Forest XGBoost

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Arm motion recognition based on attitude angle sensor 24

Arm motion recognition based on attitude angle sensor

引用

13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Niu, Weikun Zhang, Feng Yang, Guang Xie, Qiang Li, Yonggang Key labortory of recognition and intelligence China Electronics Technology Group Corporation Information Science Research Institute Beijing China Beijing Sanchuan Weiwei Technology Co. LTD Beijing100086 China

ISBN: (纸本)9798400717482

In this paper, the data-driven predictive maintenance technology is applied to the motion recognition of assembly line workers. Specifically, the motion data (acceleration, angular velocity and angle) of workers completing three types of arm movements (two counter-clockwise turns, one turn or one and a half turns, and straight line movement) were collected by the attitude angle measurement sensor. For this data set, an algorithm combining continuous wavelet transform and image deep learning model (pre-trained GoogleNet) has been proposed, and the algorithm is compared with the support vector machine based on the statistical characteristics of discrete wavelet transform and the one-dimensional convolutional network model. It is concluded that the proposed scheme can effectively identify the three kinds of typical movements performed by workers (the average test recognition accuracy is around 93%), which is significantly better than the other comparison models. Through feature analysis, it can be concluded that the scheme combining continuous wavelet transform and image processing deep learning model proposed in this paper has better distinguishable feature space compared with the contrastive models. The scheme proposed in this paper can be extended to predictive maintenance scenarios based on time series data type sensors. © 2024 Copyright held by the owner/author(s).

关键词： Predictive maintenance

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Vehicle Pseudonym Change Mechanism Based on Dynamic Game

Vehicle Pseudonym Change Mechanism Based on Dynamic Game

引用

2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Zhao, Zheng Wang, Junren Mao, Qian Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Liaoning University Shenyang China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Intelligent Transportation Systems (ITS), particularly vehicular ad-hoc networks, play a crucial role in improving road safety and traffic efficiency. However, transmitting vehicle location and identity data poses significant privacy risks, potentially allowing attackers to exploit this information. In this study, we developed a user-centric privacy model based on information entropy, enabling vehicles to monitor real-time location privacy levels. We also proposed a decision algorithm for pseudonym changes, dynamically identifying a cooperative set of vehicles for pseudonym alteration. Comparative experiments using SUMO and NS-3 simulations show that our scheme enhances location privacy while reducing pseudonym change frequency. This research provides valuable theoretical and technical foundations for ITS. © 2025 SPIE.

关键词： Vehicular ad hoc networks

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Addressing Cybersecurity Challenges in 6G Networks Through AI-Driven Adaptive Defense Mechanisms and Quantum-Resilient Protocols

Addressing Cybersecurity Challenges in 6G Networks Through A...

引用

2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Sankalp, M.R. Lokapal, G. Mohan, B.A. Basavaraj, G.N. Bms Institute of Technology & Management Department of Information Science Bengaluru India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538538

The upcoming 6G networks will offer unmatched connectivity features along with virtually no latency delays while allowing fast transmissions that lead to breakthrough technologies including autonomous vehicles and smart cities and extended reality. The advancement of fifth-generation networks introduces crucial cybersecurity problems because APTs and multiple system weaknesses persist together with potential threats from quantum computers. Available security solutions must incorporate innovative methods that create resistant security infrastructure structures for current and future security threats. This study delivers both intensive evaluations of modern techniques and actual use examples then develops new integrative frameworks which link American intelligence with quantum-resistant systems. The research explores how these solutions perform in heterogeneous decentralized systems and provides practical information about deploying these solutions together with performance evaluation metrics. The research combines recent academic findings leading to secure 6G networks which solve the dual security problems of modern-day AI cyber threats and quantum vulnerabilities. © 2025 IEEE.

关键词： Qubits

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Parallel Implementation of Sieving Algorithm on Heterogeneous CPU-GPU computing Architectures 19th

Parallel Implementation of Sieving Algorithm on Heterogeneou...

引用

19th international conference on information Security Practice and Experience

作者： Wu, Mengsi Li, Pei Chen, Jiageng Yao, Shixiong Cent China Normal Univ Wuhan 430079 Peoples R China

ISBN: (纸本)9789819790524;9789819790531

Sieving algorithms currently represent the fastest approach of solving the Shortest Vector Problem (SVP). However, current sieving algorithms exclusively utilize either CPUs or GPUs. This paper introduces a novel sieving approach tailored to CPU+GPU heterogeneous computing platforms. We constructed a runtime system capable of concurrently executing both CPU and GPU versions of the sieving algorithm. The GPU version of the sieving algorithm reduces the demand for graphics memory by efficiently transferring data in batches. We used two computing platforms to evaluate our method: Hannibal is equipped with an AMD Ryzen 7 5800H CPU and an NVIDIA RTX 3060 GPU;Zeus is equipped with an Intel Xeon Platinum 8176M CPU and two Integrated Matrox G200eW3 Graphics Controllers. The experimental results show that, compared to the classical sieving algorithm implementation in G6K, the proposed method achieves a minimum speedup of 7.2x (for 30-dimensional SVP) and a maximum of 588x (for 120-dimensional SVP) on Hannibal and a minimum speedup of 4.3x (for 30-dimensional SVP) and a maximum of 1230x (for 120-dimensional SVP) on Zeus.

关键词： SVP Sieving Heterogeneous computing CUDA

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Dual-Channel Convolutional Neural Network for Multi-Class Drone Signal Classification Using Time and Frequency Domain Features 24

Dual-Channel Convolutional Neural Network for Multi-Class Dr...

引用

13th international conference on Networks, Communication and computing, ICNCC 2024

作者： Wuhao, Li Yong, Jia Chen, YaFeng Chengdu University of Technology Sichuan Chengdu China Sichuan Koobee Communication Equipment Co. Ltd. Sichuan Yibin China

ISBN: (纸本)9798400717352

This study proposes a dual-channel convolutional neural network (CNN) for multi-class drone signal classification, aimed at improving classification accuracy by extracting features from different perspectives of the original signal. The first channel uses a one-dimensional convolutional neural network (1D CNN) to capture local spatial features directly from the raw one-dimensional signal. The second channel transforms the one-dimensional signal into a two-dimensional image through a Short-Time Fourier Transform (STFT), allowing the network to extract frequency domain information and spatial variation features. In the feature fusion stage, the features from both channels are integrated and passed to fully connected layers for nine-class classification. The model was trained and tested on the publicly available DroneRFa dataset, achieving a classification accuracy of 99.06% after feature fusion. This result demonstrates that the dual-channel structure can effectively capture and integrate distinct feature information to achieve high-precision drone recognition. The proposed method highlights the advantage of fusing time-domain and frequency-domain features, providing an effective solution for multi-class drone recognition tasks. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Drones

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Enhancing Timeliness and Accuracy in Blood Transportation through Drone technology in Saudi Arabia's Government Clinic 22

Enhancing Timeliness and Accuracy in Blood Transportation th...

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22nd international Learning and technology conference, L and T 2025

作者： Alseraihi, Meaad Aljadani, Amjaad Alsahafi, Samar Alzein, Maryam Elhag, Salma King Abdulaziz University Faculty of Computing & Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

Timely blood sample transportation is crucial for healthcare, particularly in emergencies, in Saudi Arabia's government clinics, the current process involves daily noon collections by SPL logistics representatives, causing delays in testing and limited availability for urgent cases. Through consultations with a healthcare professional, we identified Key inefficiencies include restricted collection times, absence of onsite laboratories, and lengthy transportation, often exceeding an hour. This study applied business process re-engineering (BPR) to address these issues by integrating drone technology, using the Business Process Model and Notation (BPMN) framework, the current process (AS-IS) was analyzed, and a future model (TO-BE) incorporating drones was developed, simulations using Bizagi revealed a significant reduction in average process time from 3 hours and 20 minutes to 1 hour and 19 minutes, the findings highlight the potential of drones to revolutionize healthcare logistics, reducing delays and improving patient outcomes, future research proposes a hybrid approach, integrating drones with traditional logistics systems for enhanced efficiency and scalability. © 2025 IEEE.

关键词： Reengineering

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