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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Han, Hao Lu, Feng Deng, Yanhan Luo, Xiaofang Jin, Hai Tu, Wei Xie, Xia School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan430074 China Tongji Medical College Huazhong University of Science and Technology Tongji Hospital Wuhan430022 China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9798350358780

In the medical realm, the pivotal role of pathological Whole Slide Images (WSIs) in detecting cancer, tracking disease progression, and evaluating treatment efficacy is indisputable. Nevertheless, the identification and quantification of lesion areas in these gigapixel WSIs present a significant challenge due to their substantial size and the intricate details of lesions. To address these issues, we developed a novel multi-resolution and multi-scale cross fusion network (M2CF-Net), adept at managing large-scale pathological WSIs and capturing both fine details and context. Our model particularly focuses on segmenting local lymphocyte infiltration lesions in pathological WSIs of patients diagnosed with primary Sjogren's syndrome. By employing a patch-based training approach and combining interconnected elements via a multi-scale fusion technique, we enhance our model's capacity to detect and analyze structures and features in minor salivary gland section WSIs. Extensive experiments and ablation studies conducted on real-world clinical datasets affirm our model's superior accuracy in identifying lymphocyte-infiltrated regions over state-of-the-art models, with a performance improvement of up to 4.32% in terms of the Dice Similarity Coefficient. © 2023 IEEE.

关键词： Pathology

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A Comparison of Cloud Security Mechanisms 2

A Comparison of Cloud Security Mechanisms

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2nd International Conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Saravanan, D. Maragatharajan, M. Nirmala, E. Naveen, P. Francis Saviour Devaraj, A. School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Kothrikalan India Department of Electrical and Electronics Engnieering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India Department of IT College of Computing & Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (纸本)9798350340235

Cloud computing is a broad term that can be understood differently depending on the context. Its primary goal is to reduce capital expenses on infrastructure, operating systems, deployment, and storage. The National Institute of Standards and Technology (NIST) defines cloud computing as on-demand network access to a shared pool of computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing operates using virtualization and parallel computing principles and provides services like IaaS, PaaS, SaaS, and NaaS. The cloud is classified into public, private, hybrid, and community clouds, each with its own unique features. The major issue of cloud computing is to transfer the data with security breaches. Various security systems are available to protect the data security in the cloud. This research study has discussed some security models like Advanced encryption standard algorithm, Biometric model, Elliptic-curve cryptography with Diffie-Hellman algorithm and a new technique vector-matrix based encryption algorithm. The execution time for these algorithms were compared and results were discussed. © 2023 IEEE.

关键词： Advanced encryption standard algorithm Biometric model Cloud security Decryption Diffie-Hellman algorithm Elliptic-curve cryptography Hybrid cloud vector-matrix based encryption algorithm

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Forecasting Meteorological Drought Using Weather Data With a Machine Learning Framework

Forecasting Meteorological Drought Using Weather Data With a...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Anirudh Krishnan V Siva Jyothis Deepa Gupta Aarathi Rajagopalan Nair Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Natural calamities like drought are severely impacted by the unpredictability of climate change and its adverse consequences. Drought is one of the major calamities that impact the social, economic, and environmental spheres across the globe. This necessitates accurate prediction methods to mitigate their effects. In order to forecast drought using meteorological data, this paper investigates a variety of machine learning methods, ranging from simple to intricate algorithms. The study focused on exploring the capabilities of feature selection and data balancing techniques to attain better scores. Among the different models, Random forest when combined with SMOTE achieved an accuracy of 0.82,while stacking approach emerged as the most effective with an accuracy of 0.83 and F1-score of 0.71. These findings underscores the importance of machine learning frameworks to tackle the challenges posed by drought and take proactive methodologies to mitigate its adverse impacts.

关键词： Accuracy Machine learning algorithms Disasters Droughts Stacking Weather forecasting Prediction methods Prediction algorithms Socioeconomics Random forests

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Distributed quantum architecture search

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Physical Review A 2024年第2期110卷 022403-022403页

作者： Haozhen Situ Zhimin He Shenggen Zheng Lvzhou Li College of Mathematics and Informatics School of Electronic and Information Engineering Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area (Guangdong) Shenzhen 518045 China Institute of Quantum Computing and Software School of Computer Science and Engineering

Variational quantum algorithms, inspired by neural networks, have become a novel approach in quantum computing. However, designing efficient parameterized quantum circuits remains a challenge. Quantum architecture search tackles this by adjusting circuit structures along with gate parameters to automatically discover high-performance circuit structures. In this study, we propose an end-to-end distributed quantum architecture search framework, where we aim to automatically design distributed quantum circuit structures for interconnected quantum processing units with specific qubit connectivity. We devise a circuit generation algorithm which incorporates TeleGate and TeleData methods to enable nonlocal gate implementation across quantum processing units. While taking into account qubit connectivity, we also incorporate qubit assignment from logical to physical qubits within our quantum architecture search framework. A two-stage progressive training-free strategy is employed to evaluate extensive circuit structures without circuit training costs. Through numerical experiments on three VQE tasks, the efficacy and efficiency of our scheme is demonstrated. Our research into discovering efficient structures for distributed quantum circuits is crucial for near-term quantum computing where a single quantum processing unit has a limited number of qubits. Distributed quantum circuits allow for breaking down complex computations into manageable parts that can be processed across multiple quantum processing units.

关键词： Machine learning Quantum circuits

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Guest Editorial - Special Issue on IEEE AIVR 2021

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International Journal of Semantic computing 2023年第1期17卷 93-94页

作者： Hu, Min-Chun Hürst, Wolfgang Sheu, Phillip C.-Y. Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department for Information and Computing Sciences Utrecht University Utrecht Netherlands Samueli School of Engineering University of California IrvineCA United States

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Document Deterioration Classification Using Gabor Filters and Ensemble Classifiers Integrated with LIME

Document Deterioration Classification Using Gabor Filters an...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Inuguri, Anantha Hothri Narendran, Advik Saga, Hemanth Sivan, Remya Pati, Peeta Basa Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

In this era, when document preservation is of grave importance, the introduction of innovations to improve the quality of palm-leaf manuscripts becomes significant for the preservation of cultural heritage. These manuscripts deteriorate in various ways, which makes them difficult to preserve and read. The unique patterns of deterioration that can take place across each section of the same document are not easily captured by conventional methods. The current approach adopted uses chunks of palm-leaf manuscripts from documents and classifies them into deterioration types using multiple advanced machine-learning models. Multiple ensemble methods are used further to tune and increase the accuracy. After that, chunks are reassembled and tailored image enhancement is done based on the results. Wrapper forward feature selection to enhance the classification quality of the chunks and additionally Local Interpretable Model-agnostic Explanations (LIME) explainability is used to analyze features contributiing to the different classes. The proposed model outperforms traditional classification techniques by integrating chunk-wise classification with ensemble methods. It achieves a significant accuracy improvement, with Bagging Random Forest (RF) yielding the highest accuracy of 92%, compared to Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) This demonstrates the model's superior ability to handle the complexities of palm-leaf manuscript deterioration, providing a more accurate and reliable classification for document preservation. © 2025 The Author(s).

关键词： Support vector machines

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Cloud based Effective Environmental Monitoring of Temperature, Humidity and Air Quality using IoT Sensors 5

Cloud based Effective Environmental Monitoring of Temperatur...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Dharshani, J. Annamalai, Suresh Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Chengalpattu Tamil NaduKattankulathur Chennai603203 India

ISBN: (纸本)9798400709418

Pollution levels have risen over time due to a number of factors, including a rising human population, greater automobile usage, industrialization, and urbanization, all of which have negative consequences for human health. In this work, we will create an IoT-based air pollution monitoring system to detect the presence of dangerous chemicals in the environment, including carbon monoxide (CO), cigarette smoke, ethanol, benzene, and ammonia (NH3), anytime any of these parameters falls below a predefined threshold. Keeping tabs on the air quality with ease thanks to the LCD display and online data. The pollutant level can be tracked remotely through a computer or mobile device in this IoT project. Data gathering and exchange had to be optimized due to the high demand for internet-based services. When discussing about the "Internet of Things,"it refers to the ever-expanding network of connected items that can collect and exchange data via embedded sensors. It has found widespread application across all industries in recent years and is essential to the planned environmental monitoring system. Using the low-power, low-cost "Arduino UNO"microcontroller to gather and transmit data from various sensors, the Internet of Things (IoT) converges with cloud computing to provide a revolutionary approach to improved data management. Thingspeak, an open-source website, is used to update the measurements and parameters. Thingspeak is a free and open-source application programming interface (API) for storing and retrieving sensor data over the Internet over the HTTP protocol. © 2023 ACM.

关键词： Ammonia

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Adversarial Network Pruning by Filter Robustness Estimation 48

Adversarial Network Pruning by Filter Robustness Estimation

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhuang, Xinlu Ge, Yunjie Zheng, Baolin Wang, Qian Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Hubei China Alibaba Group Beijing China

ISBN: (纸本)9781728163277

Network pruning has been extensively studied in model compression to reduce neural networks' memory, latency, and computation cost. However, the pruned networks still suffer from the threat posed by adversarial examples, limiting the broader application of the pruned networks in safety-critical applications. Previous studies maintain the robustness of the pruned networks by combining adversarial training and network pruning but ignore preserving the robustness at a high sparsity ratio in structured pruning. To address such a problem, we propose an effective filter importance criterion, Filter Robustness Estimation (FRE), to evaluate the importance of filters by estimating their contribution to the adversarial training loss. Empirical results show that our FRE-based Robustness-aware Filter Pruning (FRFP) outperforms the state-of-the-art methods by 12.19%∼37.01% of empirical robust accuracy on the CIFAR10 dataset with the VGG16 network at an extreme pruning ratio of 90%. © 2023 IEEE.

关键词： adversarial example adversarial training Neural network pruning structured pruning

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An Efficient Detection and Classification of Plant Diseases using Deep Learning Approach

An Efficient Detection and Classification of Plant Diseases ...

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2023 International Conference on Evolutionary Algorithms and Soft computing Techniques, EASCT 2023

作者： Jagadeesan, S. Deepakraj, E. Ramalingam, Venkadeshan Venkatachalam, Ilayaraja Vivekanandan, Manojkumar Manjula, R. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India University of Technology and Applied Sciences - Shinas Branch Information Technology Department Oman Vellore Institute of Technology School of Computer Science and Engineeing Vellore India School of Engineering and Applied Sciences Srm University-AP Department of Computer Science & Engineering Andhra Pradesh Amaravati India Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technology Kattankulathur India

ISBN: (纸本)9798350313413

Crop failure caused by disease-causing pests is an important problem. Farmers struggle with disease management and detection due to inadequate interventions. The goal is to create an automatic system to efficiently identify plant diseases from photos while reducing crop losses and increasing productivity. Machine learning algorithms offer a faster and cheaper alternative to visual inspection by experts. The main goal is to perform image analysis for early diagnosis and effective disease control. The use of CNN architecture for plant disease classification and detection offers a promising solution for plant health monitoring and risk mitigation. Given the threats to crop productivity and global food security, reliable methods for early detection and accurate classification are essential. CNNs enable efficient analysis of vast plant image databases, enabling accurate plant disease identification with speed and accuracy. The multi-layer CNN approach extracts feature and refines the representation, facilitating accurate prediction and accurate diagnosis. Transfer learning methods accelerate system development and allow adaptation to plant disease-specific databases. Combining computer vision algorithms with CNN architecture enables real-time monitoring, early disease detection and targeted intervention, reducing yield losses and improving crop management. This approach uses AI, image analysis, and plant pathology to solve the challenges of sustainable agriculture and plant diseases. System performance is measured by various performance metrics. © 2023 IEEE.

关键词： CNN-Based Approach Error Rate Plant disease Precision ROC

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Correlation assisted in-line holographic imaging through scattering medium 13

Correlation assisted in-line holographic imaging through sca...

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Holography, Diffractive Optics, and Applications XIII 2023

作者： Vinu, R.V. Melath, Rathinn Nair Raghav, Sahmita Gopakumar, G. Chen, Ziyang Pu, Jixiong College of Information Science and Engineering Fujian Provincial Key Laboratory of Light Propagation and Transformation Huaqiao University Fujian Xiamen361021 China Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Kerala Amritapuri India

ISBN: (纸本)9781510667853

We propose and experimentally demonstrate a technique for the single-shot imaging through a scattering medium based on speckle correlation assisted digital in-line holography. The proposed method enables the reconstruction of the complex amplitude information of the object located at any arbitrary planes behind the scattering medium by retrieving the digital in-line hologram. The technique exploits the potential of speckle correlation approaches and machine learning schemes for the efficient reconstruction of complex amplitude information from the digital in-line hologram scrambled in the speckle pattern. The complex amplitude imaging capability of the proposed technique is theoretically introduced and experimentally validated by imaging various standard and unknown objects. © 2023 SPIE.

关键词： Speckle

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