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A Machine learning-Based Error Mitigation Approach for Reliable Software Development on IBM's Quantum Computers 32

A Machine Learning-Based Error Mitigation Approach for Relia...

引用

32nd ACM international conference on the Foundations of Software engineering (FSE)

作者： Muqeet, Asmar Ali, Shaukat Yue, Tao Arcaini, Paolo Simula Res Lab Oslo Norway Univ Oslo Oslo Norway Oslo Metropolitan Univ Oslo Norway Natl Inst Informat Tokyo Japan

ISBN: (纸本)9798400706585

Quantum computers have the potential to outperform classical computers for some complex computational problems. However, current quantum computers (e.g., from IBM and Google) have inherent noise that results in errors in the outputs of quantum software executing on the quantum computers, affecting the reliability of quantum software development. The industry is increasingly interested in machine learning (ML)-based error mitigation techniques, given their scalability and practicality. However, existing ML-based techniques have limitations, such as only targeting specific noise types or specific quantum circuits. This paper proposes a practical ML-based approach, called Q-LEAR, with a novel feature set, to mitigate noise errors in quantum software outputs. We evaluated Q-LEAR on eight quantum computers and their corresponding noisy simulators, all from IBM, and compared Q-LEAR with a state-of-the-art ML-based approach taken as baseline. Results show that, compared to the baseline, Q-LEAR achieved a 25% average improvement in error mitigation on both real quantum computers and simulators. We also discuss the implications and practicality of Q-LEAR, which, we believe, is valuable for practitioners.

关键词： Software engineering Error Mitigation Quantum computing Machine learning Quantum noise

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Enhancing Healthcare through the Symbiosis of Blockchain and Machine learning in Preprocessing Skin Disease Images: A Review 15

Enhancing Healthcare through the Symbiosis of Blockchain and...

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rokade, Sonali Mishra, Nilamadhab School of Computer Science and Engineering Vellore Institute of Technology Bhopal India

ISBN: (纸本)9798331300579

Recent advancements in skin disease identification leverage machine learning for automated diagnosis. However, safeguarding the integrity of sensitive medical data remains a top priority. This paper explores the integration of machine learning and blockchain in skin disease identification preprocessing, promising trust, transparency, and accuracy in dermatological diagnosis. The preprocessing workflow starts with gathering a diverse, labeled skin image dataset, followed by data cleaning and augmentation to enhance quality and quantity. Standardization methods like resizing and normalization ensure data consistency, while addressing class imbalances prevents model bias. Blockchain technology is introduced to secure data integrity. Each skin image and its metadata are encrypted and timestamped on a decentralized blockchain, guaranteeing tamper-proof authenticity. Machine learning models, typically based on CNNs, extract features and classify diseases, benefiting from high-quality preprocessed data. During training, blockchain securely records model parameters and progress, ensuring transparency and auditability, crucial for medical accountability and *** fusion of machine learning and blockchain enhances diagnostic accuracy while addressing data privacy and security concerns, potentially revolutionizing dermatological healthcare with a trustworthy, automated diagnosis system. © Grenze Scientific Society, 2024.

关键词： Data integrity

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Federated learning in Big Data with IoT for Intrusion Detection 6

Federated Learning in Big Data with IoT for Intrusion Detect...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Govindaram, Anitha Prasath, J.S. Suganya, A. Jayasakthi, K. Rajkumar, N. Jose Anand, A. Thandalam Chennai602105 India Department of Computer Science and Engineering Sapthagiri NPS University Chikkasandra Karnataka Bengaluru560057 India Department of Artificial Intelligence and Data Science Anand Institute of Higher Technology Kazhipattur Tamil Nadu Chengalpattu603103 India Department of ECE KCG College of Technology Karapakkam Tamil Nadu Chennai600097 India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvalluvar Tamil Nadu Avadi600062 India

ISBN: (纸本)9798331522667

Artificial Intelligence (AI) part of Federated learning (FL) builds on distributed data and modelling to deliver learning to the edge of the device. Even though FL has been celebrated as the beginning of AI, it has not yet gained mainstream adoption, mostly because of concerns about privacy and security. Security and privacy issues with the FL technique need to be found, examined, and recorded before at this point, we can expect to see significant progress in this field of study or widespread adoption of the FL approach. This article aims to afford a complete review of the methods discussed with FL for huge data by introducing studies from six angles: blockchain, privacy, hybrid approach, VFL, FL, and foundation design and device heterogeneity. This survey can address the challenges of FL, offer their predictions for the future of research on the subject, outline how FL is now being put to use in the real world, and with most salient features. © 2025 IEEE.

关键词： Federated learning

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A Heterogeneous Semi-Supervised learning Network for Road Damage Detection 9

A Heterogeneous Semi-Supervised Learning Network for Road Da...

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9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Guo, Chen Su, Yiheng Zuo, Chen Chang'an University School of Information Engineering Xi'an China

ISBN: (纸本)9798350376548

Road damage detection is important for intelligent traffic systems and the sustainability of transportation infrastructure. Utilizing deep learning methodologies, we can achieve automation and improve efficiency in the context of road damage detection. However, conventional deep learning-base object detection methods require extensive labeled data and struggle with generalization. In order to alleviate the demand of data annotations, we introduce a semi-supervised road damage detection method using an ensemble of heterogeneous detectors. Cascade RCNN, YOLO, and DETR models are employed for diverse learning and reduced cognitive bias. Iterative training and pseudo-label generation have a positive effect on model robustness. An adaptive weight fusion framework improves detection accuracy. Validated on a drone-captured highway database, the method indicates superior performance. Our method trained on a dataset of 200 labeled images and 5000 unlabeled images, reaches 89% mAP50, marking a 21% enhancement compared to Soft Teacher. It provides an economical, precise, and dependable approach for road maintenance and repair operations. © 2024 IEEE.

关键词： Semi-supervised learning

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Exploration of Blended teaching in Data Mining Course Based on STEM Education Concept 2023

Exploration of Blended Teaching in Data Mining Course Based ...

引用

8th international conference on Information and Education Innovations, ICIEI 2023

作者： Zhu, Yingxue Liu, Mingyou Wang, Yue Wu, Tao School of Biology and Engineering Guizhou Medical University China

ISBN: (纸本)9798400700613

With the deep development of big data, artificial intelligence and other technologies, data thinking ability, engineering practice ability and theoretical innovation ability have become the core educational objectives for colleges and universities to cultivate high quality composite "new engineering"talents. In view of the current situation that the teaching content of traditional computer courses is boring and difficult to understand, the teaching is teacher-centered, which is difficult to stimulate students' interest in learning, and the course content is limited by credit hours to improve the teaching effect, this paper takes the teaching mode design of data mining course as an example to explore the teaching design of data mining course in colleges and universities based on the concept of STEM engineering education, and proposes an inquiry-based teaching design concept which is student-centered, problem-oriented and case-driven, and uses online and offline blended teaching method to realize students' personalized learning needs and encourage students to realize active learning, aims to cultivate students' data thinking ability, engineering practice ability and theoretical innovation ability, and to cultivate teamwork and the ability to use data mining knowledge to analyze and solve problems in the process of project practice, so as to explore a new path for cultivating high-quality composite "new engineering"talents. © 2023 ACM.

关键词： Data mining

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An analysis comparing CT and X-ray images for COVID-19 identification employing deep learning and image processing methods 3

An analysis comparing CT and X-ray images for COVID-19 ident...

引用

3rd international conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2024

作者： Prabu, R. Dhinakar, P. Bharath Institute of Higher Education and Research Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350391565

This study presents a comparative analysis of deep learning approaches for the identification of COVID-19 pneumonia in medical imaging, specifically focusing on CT and X-ray modalities. To aid healthcare professionals in quickly identifying infected individuals during the COVID-19 pandemic, accurate and efficient diagnostic tools are urgently required. In order to identify COVID-19 more quickly and accurately, deep learning algorithms combined with medical imaging data produced results. Computed Tomography (CT) and X-ray imaging are commonly used modalities for assessing lung involvement in COVID-19 patients, each offering unique advantages and challenges. Leveraging deep learning techniques, such as convolutional neural networks (CNNs), this analyzes the performance of models trained on large datasets of labeled CT and X-ray images. Our analysis includes preprocessing methods, model architectures, training procedures, and evaluation metrics to assess the efficacy of these approaches. Furthermore, this discusses the clinical implications, challenges, and ethical considerations associated with integrating deep learning-based diagnostic tools into routine clinical practice. © 2024 IEEE.

关键词： COVID-19

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Efficiently Identifying Binary Similarity Based on Deep Hashing and Contrastive learning 8

Efficiently Identifying Binary Similarity Based on Deep Hash...

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8th international conference on Cloud computing and Big Data Analytics (ICCCBDA)

作者： Xiong, Jiaqi Cheng, Shaoyin Gao, Han Zhang, Weiming Univ Sci & Technol China CAS Key Lab Elect Space Informat Hefei Peoples R China Anhui Prov Key Lab Cyberspace Secur Situat Awaren Hefei Peoples R China

ISBN: (纸本)9781665455336

Binary similarity is to identify the semantic similarities of two or more binary code snippets. In recent years, deep learning-based methods have shown promising results. They formalize code similarity as the nearest neighbor retrieval task, and the overall workflow can be divided into two stages: 1) feeding the code snippets into the embedding model to get the corresponding high-dimensional vectors as fingerprints (i.e., constructing the codebase). 2) using the codebase for nearest neighbor retrieval to get the top-k results. Most existing studies only focus on the first stage (more specifically, the embedding model) while ignoring the overhead of the retrieval stage. In real-world scenarios, the codebase could be quite large and contain massive embeddings, which keeps the precise nearest neighbor retrieval prohibitive expensive. To mitigate the issue above, this paper proposes a novel approach, dubbed BinCH, which can efficiently perform code search without sacrificing accuracy.

关键词： Big Data Reverse engineering Deep learning Deep Hashing Binary Diffing

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Bridging the Modality Gap: Generative Adversarial Networks for T1-T2 MRI Image Translation 15

Bridging the Modality Gap: Generative Adversarial Networks f...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Pandey, Mayank Kunda, Naga Sai Shreya Pranave, K.C. Singh, Tripty Nair, Rekha R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Alliance University Alliance College of Engineering and Design Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

CycleGAN, a significant deep learning model, has transformed medical imaging by providing a remedy for translating MRI images between different modalities. Its ability to transform T1-weighted to T2-weighted images is crucial in healthcare environments where obtaining multiple scans can be time-consuming and burdensome for patients. This can be achieved by utilizing CycleGAN, which generates images that contains vital information that will help the healthcare professionals to diagnose the patient without exposing them to further radiations. This unleashes an effective and safer way of diagnosing patients. The adaptability of CycleGAN also allows scalability and the ability to customize it for different types of medical images, where scanning can be achieved with less exposure to radiation, without loosing or compromising any important information. © 2024 IEEE.

关键词： Discriminator GAN Generator Magnetic Resonance Image T1 T2

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Machine learning Assisted Sanitizer Quality Identification Using Permittivity Measurement 11

Machine Learning Assisted Sanitizer Quality Identification U...

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11th IEEE international conference on Advances in computing and Communications, ICACC 2024

作者： Thushara, H.P. Mridula, S. Neema, K. CUSAT Division of Electronics School of Engineering Kerala India CUSAT Department of Electronics Kerala India

ISBN: (数字)9798350379136

ISBN: (纸本)9798350379136

Sanitizers play a crucial role in hygiene and the prevention of infectious diseases by reducing harmful microorganisms on surfaces. This paper focuses on alcohol-based sanitizers, specifically those containing ethanol and isopropyl alcohol, known for their rapid action and broad spectrum efficacy. The effectiveness of these sanitizers depends on the alcohol content, with water enhancing their performance. Traditional quality detection methods are accurate but time-consuming. This work proposes a support vector machine based classification model combined with a complex permittivity measurement system to assess sanitizer quality. The model achieves 97.2% accuracy for a frequency range of 0.2-20 GHz at 25°C, with improved accuracy of 99% in the 2-5 GHz range, identifying this as the optimal frequency range for sanitizer classification. This method is compatible with any permittivity measurement system with measurement error ±15%. Significant time savings in quality assessment is also achieved. © 2024 IEEE.

关键词： Permittivity measurement

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A Machine learning-Based Approach for Anomaly Detection for Secure Cloud computing Environments

A Machine Learning-Based Approach for Anomaly Detection for ...

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2023 international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Parameswarappa, Priya Shah, Taral Lanke, Govinda Rajulu University of the Cumberlands School of Technology United States Pace Univerty School of Information System United States Birla Institute of Technology and Science Data Science and Engineering India

ISBN: (纸本)9781665474511

The concept of "cloud computing"has been presented as a promising strategy for providing online service hosting and distribution. Despite the widespread adoption of cloud computing, security remains a top priority. Several secure methods have been devised to safeguard communication in such scenarios, with the majority of these solutions based on attack signatures. Unfortunately, these technologies cannot always detect every possible danger. A machine learning method was recently outlined. The judgment could be inaccurate if the training set is missing examples from a certain category. In this research, an innovative firewall strategy for safe cloud-based computing is presented using machine learning system. The proposed methods estimate the final assault category categorization by combining the judgments of the nodes from the past with the decision of the machine learning algorithm in the present, a technique termed most frequent decision. Both learning efficiency and system precision are improved by this method. Our results are based on UNSW-NB-15, a publicly available dataset. According to the evidence provided by our data, it improves anomaly detection by 97.68 percent. A Machine learning-Based Approach for Anomaly Detection for Secure Cloud computing Environments © 2023 IEEE.

关键词： Anomaly detection

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