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检索条件"机构=dept. of Computer Computer Systems Engineering"

共 66560 条记录，以下是441-450 订阅

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WANE: Workload Adaptive Neuro-genetic Engine for Container Usage Prediction 5

WANE: Workload Adaptive Neuro-genetic Engine for Container U...

引用

5th International Conference on Advances in Electrical engineering and computer Applications, AEECA 2024

作者： Park, Soyeon Bahn, Hyokyung Ewha University Dept. of Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350355253

Microservice architectures in Kubernetes-based container environments often face challenges in optimally allocating resources due to fluctuating workload usage. Traditional methods, which rely on heuristic computing resource requests, typically result in overprovisioning and inefficient utilization. Predicting the actual runtime resource demand of containerized applications accurately is challenging. To address this issue, the authors introduce a novel hybrid cloud resource prediction engine that combines the strengths of genetic algorithms and Bayesian neural networks. Trained with real-world trace data, the proposed model outperforms existing techniques like ARIMA and exponential smoothing, particularly in reducing the risk of underprediction. Notably, these enhancements significantly improve CPU prediction accuracy, demonstrating potential for optimizing resource allocation and enhancing cost efficiency in cloud environments. © 2024 IEEE.

关键词： Resource allocation

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An In-dept. Exploration of Machine Learning Algorithms and Performance Evaluation Approaches for Personalized Diabetes Prediction

An In-Depth Exploration of Machine Learning Algorithms and P...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Kaur, Inderdeep Ali, Aleem Chandigarh University Dept. of Computer Science and Engineering Kharar India

ISBN: (纸本)9798350376470

Diabetes is now recognised as a major worldwide health problem, demanding effective early detection and prediction measures to limit its repercussions. Machine learning algorithms have shown to be excellent tools in healthcare for creating prediction models. This paper delves into an extensive investigation of machine learning (ML) algorithms and methodologies for assessing their performance in the context of personalized diabetes prediction. Different machine learning models are rigorously explored and evaluated for how well they can customise forecasts to the unique characteristics of individual patients. The study also critically assesses several performance evaluation techniques in an effort to improve the accuracy and reliability of diabetes prediction algorithms. Furthermore, interpretability problems and class imbalance are addressed as challenges and limitations to the application of machine learning in the prediction of diabetes. This study aims to motivate researchers, facilitating a deeper understanding of disease prediction algorithms and conduct related research. © 2024 IEEE.

关键词： Prediction models

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Performance Evaluation of DL Models of Low-Size Datasets for AMC in MIMO System

Performance Evaluation of DL Models of Low-Size Datasets for...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Helmy, Islam Choi, Wooyeol Chosun University Dept. of Computer Engineering Gwangju Korea Republic of

ISBN: (纸本)9798350324136

Automatic modulation classification (AMC) is one of the foremost challenges in multi-input multi-output (MIMO) systems. In literature, various approaches have been presented where the deep learning (DL) models show remarkable performance. However, the existing methods mainly focus on training using large datasets, which is insignificant in high-mobility environments where rapid changes occur. In this study, we investigate the performance of existing DL models for small datasets and reduce the complexity by eliminating the equalizer. We apply two DL models to estimate the channel modulation among four modulation techniques. We also vary the number of transmitters for different datasets where the number of receiver antennas is 4 and 8, respectively. Furthermore, we verify the signal-To-noise ratio (SNR) of 0 to 40 dB. The results show that the convolutional neural network (CNN) performs better than the multi-layer perceptron (MLP) applied to the different datasets. © 2024 IEEE.

关键词： Automatic modulation classification deep learning MIMO

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Cat-CNN: Human Eye Cataract Detection from Color Fundus Photograph with Deep CNN with Optimized Cascaded Network

Cat-CNN: Human Eye Cataract Detection from Color Fundus Phot...

引用

2024 IEEE International Conference on Signal Processing, Information, Communication and systems, SPICSCON 2024

作者： Islam, Md Sariful Bappy, Md Tusher Ahmad Shawon, Jubayer Ahmed Hasan, Mehedi Rahman, Wahidur Akter, Lija Azad, Mir Mohammad Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of ICE Bangladesh Army University of Engineering and Technology Natore Bangladesh Dept. of Computer Science and Engineering Inpendent Researcher Dhaka Bangladesh

ISBN: (纸本)9798331510213

Cataracts are clouding of the lens in the eye, leading to loss of vision that can progress to blindness if not treated. This paper proposed a new method for automatic cataract detection using color fundus images and deep learning methods. A dataset of 1,105 color fundus images labeled by expert ophthalmologists was used in this process. We used seven pre-trained CNNs (DenseNet121, EfficientNetB0, MobileNetV2, InceptionV3, Xception, ResNet50, VGG16, and VGG19) for feature extraction before reducing the extracted features using PCA. We used the following combination of machine learning classifiers: SVC, RF, Decision Tree, Gaussian Naive Bayes, XGBoost, K-Nearest Neighbors, and Logistic Regression. For evaluating the models’ performance, we used accuracy, precision, recall, F1-score, and computational efficiency. For all metrics, MobileNetV2 with Random Forest achieved perfect scores: 100% accuracy, precision, recall, and F1-score, with an average processing time of 669 ms ± 28.8 ms. Thus, it can be applied in real-time applications. EfficientNetB0 with SVC gave an average accuracy of 87.33%, with the rest of the precision and recall metrics above 86%. Then, ResNet50, VGG16, and VGG19 followed with high accuracies between the range of 89.64% to 90.50%. It systemizes the proper choice of architectures of CNNs and classifiers, making the system both accurate and computationally efficient. Future work will include augmentation of the dataset, real-time support in the clinical setting, and advanced techniques for image preprocessing, generative adversarial networks. In addition, the development of an automated annotation tool, improvement of explainable AI, will further improve the deployment of robust AI systems in early diagnosis of cataracts, enhancing the outcome for patients. © 2024 IEEE.

关键词： Cataract detection CNN color fundus photography deep learning feature extraction image classification machine learning ophthalmology PCA

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A computer vision approach for classifying the degree of freshness in fruits and vegetables to reduce food waste

A computer vision approach for classifying the degree of fre...

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2024 Innovations in Intelligent systems and Applications Conference, ASYU 2024

作者： Sallam, Hamza Ghizawi, Obadah Nidal Derrija, Abdulmoez S. Sönmez, Elena Battini Dept. of Computer Engineering Istanbul Bilgi University Istanbul Turkey

ISBN: (纸本)9798350379433

The level of freshness of fruits and vegetables must be constantly monitored to reduce food waste and maximize its nutritional value. Existing approaches to this problem either consider a binary classification problem, where fruits and vegetables are classified into the two classes of"fresh" or"rotten", or a three-class problem, with"fresh", "medium", and"rotten" classes. This work challenges the three-class issue achieving an initial accuracy of 82%, using the VGG16 deep learning model, and 84%, with YOLO v5. After going through many experiments, we reached 94.58% test accuracy with VGG16, improving the current state-of-the-art performance by 11 percentage points. A detailed description of the experiments and the used algorithms, together with their hyper-parameters, is given in this paper to facilitate code reproduction. © 2024 IEEE.

关键词： Food chemistry

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Call Translator with Voice Cloning Using Transformers 9

Call Translator with Voice Cloning Using Transformers

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Khan, Nawaz Fateen Hemanth, Nandan Goyal, Navyae Pranav, K.R. Agarwal, Pooja Pes University Computer Science Engineering Dept. Bangalore India

ISBN: (纸本)9798350394474

Voice Cloning employs technology and algorithms to create an artificial or synthetic reproduction of a person's voice. To understand and mimic the distinct vocal qualities of the target speaker, including tone, pitch, cadence, and pronunciation, a machine learning model must be trained on audio samples of the speaker. This project presents itself as a revolutionary approach to the enduring problem of successful cross-lingual interactions, the paper's novel approach to speech-to-speech machine translation that incorporates voice cloning. This method allows us to synthesize speech in the target language while maintaining the original speaker's vocal characteristics. The model algorithms such as Whisper AI(WER 5% for transcription, No-Language-Left-Behind-200(44% more accurate than other models) for translation, Tacotron for speech synthesis and multiple transformers for voice cloning will be incorporated. The Voice Cloning model operates effectively on unseen voices, transcending the limitations of voice cloning will be incorporated. The voice cloning model operates effectively on unseen voices, transcending the limitations of relying solely on pre-trained models. The model successfully clones users' voices in a different language with an approximate latency of 10 seconds. © 2024 IEEE.

关键词： Speech synthesis

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A 22T Static Contention-free Conjoined Master-Slave Flip-flop for Low-Voltage, Low-Power, and Low-Area Applications

A 22T Static Contention-free Conjoined Master-Slave Flip-flo...

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2024 IEEE Asian Solid-State Circuits Conference, A-SSCC 2024

作者： Shin, Gicheol Han, Shin Jeong, Minhyeok Seo, Donguk Lee, Yoonmyung Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798350376326

Low-voltage flip-flops that operate reliably in a low-voltage regime are crucial for extending battery life in energy-constrained IoT applications. Minimizing dynamic power consumption is also vital for reducing overall SoC power. Additionally, it is important to reduce the area occupied by flip-flops as a significant portion of processor area is dedicated to sequential elements. Prior-art flip-flop designs [1-6] have aimed to be static contention-free (for robustness) and eliminate redundant clock transitions (for low power) simultaneously. However, many failed to meet one of these goals [1-4] or achieved both at a high area cost. This paper introduces a novel design that meets both requirements and achieves a smaller area compared to a conventional Transmission Gate Flip-Flop (TGFF) design. © 2024 IEEE.

关键词： Flip flop circuits

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Maximizing Bandwidth Utilization and Multipath Routing for Control Congestion in MANET 4

Maximizing Bandwidth Utilization and Multipath Routing for C...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Bundela, Sonam Singh Boghey, Rajesh Rohila, Manish Kumar Sharma, Vivek Dept. of Computer Science & Engineering Madhya Pradesh Bhopal India

ISBN: (纸本)9798331518981

This paper presents a novel Maximize Bandwidth utilization and Multipath Routing for Congestion Control (MBMRCC) scheme designed to address congestion issues in MANETs effectively. The MBMRCC scheme integrates multipath routing with adaptive rate control to enhance data throughput and minimize packet loss. In MBMRCC proposed scheme, multiple paths among sources and destinations are established using for enhance the multipath on demand protocol ability in dynamic network. This Ad hoc On-demand Multipath Distance Vector (AOMDV) routing approach distributes the data load across more than two paths, thereby reducing the congestion on any established path. Concurrently, the data rate control approach is able to handle data transmission rate based on real-time network conditions, such as node mobility, link stability, and current congestion levels. Simulation results demonstrate that the MBMRCC scheme significantly outperforms traditional single-path routing protocol AODV and Multipath routing AOMDV i.e. existing congestion control mechanisms in MANETs. Significant performance metrics such as throughput, packet delivery ratio, overhead and end-to-end delay performance are network favorable. The adaptive nature of the proposed scheme ensures robust performance in highly dynamic and heterogeneous network environments, making it a viable solution for various MANET applications, including disaster recovery, military operations, and mobile social networks. The MBMRCC scheme's ability to efficiently manage network congestion and enhance data transmission reliability underscores its potential to enhances the overall performance of MANET. © 2024 IEEE.

关键词： Economic and social effects

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A Decentralized Document Storage Platform using IPFS with Enhanced Security 8

A Decentralized Document Storage Platform using IPFS with En...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Jadhav, Swati Choudhari, Gauri Bhavik, Manasi Bura, Rishita Bhosale, Vaibhavi Vishwakarma Institute of Technology Dept. Of Computer Engineering Pune India

ISBN: (纸本)9798350391770

In response to the evolving challenges of centralized file sharing and transport-layer issues in decentralized file systems, this research paper, "A Decentralized File Sharing using IPFS with Enhanced Security,"delves into the potential of the InterPlanetary File System (IPFS) made secure with Hybrid Encryption. This decentralized approach not only addresses issues of reliability and scalability but also prioritizes enhanced security measures. By integrating cutting-edge security protocols, our study aims to fortify the IPFS framework, creating a resilient platform for secure file sharing in the digital age using algorithms such as AES and ECC-based methods. Through this exploration, we contribute to the ongoing discourse on decentralized systems, offering insights into the future of efficient and secure digital information exchange. © 2024 IEEE.

关键词： AES Blockchain Data Security ECC Encryption IPFS Privacy

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A Hybrid Model for Deciphering Doctors' Handwriting Notes Recognition 6

A Hybrid Model for Deciphering Doctors' Handwriting Notes Re...

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6th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Khanal, Santosh Bista, Rabindra Kathmandu University Dept. of Computer Science and Engineering Kavre Nepal

ISBN: (纸本)9798350389692

Handwritten Text image Recognition (HTR) in the medical profession has become the major challenge due to the complexity of doctors' handwriting styles and the critical need for precise and efficient text recognition. This study proposes an innovative approach that combines the power of Bidirectional Long Short-Term Memory networks (BLSTM) and Convolutional Neural Networks (CNN) to address these challenges effectively. The proposed model harnesses the bidirectional capabilities of BLSTMs to capture contextual dependencies within doctors' handwritten notes, enabling it to understand and interpret handwriting more accurately. Additionally, CNNs are employed for feature extraction, enabling the model to recognize salient patterns and representations within handwritten text images. This paper presents comprehensive experiments conducted on a diverse dataset of doctors' handwritten notes, demonstrating the model's superior performance compared to conventional approaches and outperforms the existing work in terms of accuracy. Two different crucial metric word error rate and character error rate are accessed through the combination of BLSTM and CNN that yields state-of-the-art accuracy, robustness to variations in handwriting styles, and remarkable adaptability across various medical documents in the context of Nepal. © 2024 IEEE.

关键词： Convolutional neural networks

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