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2025 International Conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： Khalid, Maliha Azam, Muhammad Asif, Zia Muhammad Department of Computer Science University of Agriculture Faisalabad Pakistan Department of Computer and Software Engineering Information Technology University (ITU) Lahore Pakistan

ISBN: (纸本)9781837243235

Image processing enhances digital images by extracting valuable information and improving clarity through edge recognition, color modification, and filtering techniques. These methods facilitate the creation of training images that enhance the robustness and generalization of neural networks, improving diagnostic efficiency and accuracy. The similar symptoms of chickenpox and monkeypox make early clinical diagnosis difficult, necessitating rapid and precise diagnostic techniques. This study uses deep learning models to tackle the problem of correctly diagnosing chickenpox and monkeypox, facilitating early diagnosis and quick medical intervention for improving public health and epidemic management. Models such as DenseNetl21, DenseNetl69, DenseNet201, InceptionResNetV2, InceptionV3, MobileNet, MobileNetV2, ResNet50, ResNetlOl, VGG16, VGG19, and Xception have been used to classify and distinguish conditions. Advanced image processing techniques, including object detection and data normalization, improve model generalization and resilience. Among the others, MobileNet and DenseNetl21 achieved highest accuracy rates which is 0.9943 and 0.9914 respectively, while MobileNet offered superior speed. The DenseNetl69 model has highest precision and recall which is 0.9958 and fl-score is 0.9956 with an accuracy of 0.9857. The study also demonstrates that DL models are critical in developing robust and effective systems for detecting infectious diseases, aiding in precise and efficient healthcare solutions. © The Institution of Engineering & technology 2025.

关键词： CLASSIFICATION DEEP-LEARNING IMAGE PROCESSING MONKEYPOX DATASET TRANSFER LEARNING

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Model Checking for Probabilistic Multiagent Systems

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Journal of computer Science & technology 2023年第5期38卷 1162-1186页

作者：付辰 Andrea Turrini 黄小炜宋磊冯元张立军 State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Institute of Intelligent Software Guangzhou 511455China Department of Computer Science University of LiverpoolLiverpool L693BXU.K Microsoft Research Beijing 100190China Centre for Quantum Software and Information University of Technology SydneySydney 2007Australia

In multiagent systems,agents usually do not have complete information of the whole system,which makes the analysis of such systems *** incompleteness of information is normally modelled by means of accessibility relations,and the schedulers consistent with such relations are called *** this paper,we consider probabilistic multiagent systems with accessibility relations and focus on the model checking problem with respect to the probabilistic epistemic temporal logic,which can specify both temporal and epistemic ***,the problem is undecidable in *** show that it becomes decidable when restricted to memoryless uniform ***,we present two algorithms for this case:one reduces the model checking problem into a mixed integer non-linear programming(MINLP)problem,which can then be solved by Satisfiability Modulo Theories(SMT)solvers,and the other is an approximate algorithm based on the upper confidence bounds applied to trees(UCT)algorithm,which can return a result whenever *** algorithms have been implemented in an existing model checker and then validated on *** experimental results show the efficiency and extendability of these algorithms,and the algorithm based on UCT outperforms the one based on MINLP in most cases.

关键词： probabilistic multiagent system model checking uniform scheduler probabilistic epistemic temporal logic

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Robust and Reliable Assessment of Preeclampsia Using Statistical Machine Learning 10

Robust and Reliable Assessment of Preeclampsia Using Statist...

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10th International Conference on Engineering and Emerging Technologies, ICEET 2024

作者： Bukhari, Syeda Zainab Raja, Sadia Akram, Muhammad Usman Zeb, Jahan Hassan, Taimur National University of Sciences and Technology Department of Computer and Software Engineering Pakistan Kyber Medical College Department of Gynaecology Pakistan Abu Dhabi University Department of Electrical and Computer Engineering United Arab Emirates

ISBN: (纸本)9798331532895

Artificial intelligence is increasingly prevalent, in today's world. Machine learning techniques play a role, in the healthcare field as a whole and particularly, in the areas of obstetrics and gynecology. More specifically, methods in machine learning can be used to enhance the health and well-being of pregnant women, closely monitoring their health parameters during pregnancy. In this paper, we present a solution for predicting the risk of preeclampsia, a life-threatening disorder affecting women during pregnancy. For this purpose, we collected data from three gynaecologist in Pakistan providing health services in low resource settings in which data has samples of 233 pregnant women using 13 features which are analysed during routine checkups. We adopted model comparison approach where we trained and tested our data on multiple classification models. Performance of each model was tested in terms of accuracy, precision, recall and F1-score. On the basis of these metrics, logistic regression and XGBoost were found to be the best performing models with the accuracy of 85%. © 2024 IEEE.

关键词： Adversarial machine learning

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Resource Based Automatic Calibration System (RBACS) Using Kubernetes Framework

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Intelligent Automation & Soft Computing 2023年第1期35卷 1165-1179页

作者： Tahir Alyas Nadia Tabassum Muhammad Waseem Iqbal Abdullah S.Alshahrani Ahmed Alghamdi Syed Khuram Shahzad Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science&IT Virtual University of PakistanLahore54000Pakistan Department of Software Engineering The Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan

Kubernetes,a container orchestrator for cloud-deployed applications,allows the application provider to scale automatically to match thefluctuating intensity of processing *** cluster technology is used to encapsulate,isolate,and deploy applications,addressing the issue of low system reliability due to interlocking ***-based platforms usually entail users define application resource supplies for eco container *** is a constant problem of over-service in data centers for cloud service *** operating costs and incompetent resource utilization can occur in a waste of *** revolutionized the orchestration of the container in the cloud-native *** can adaptively manage resources and schedule containers,which provide real-time status of the cluster at runtime without the user’s *** clusters face unpredictable traffic,and the cluster performs manual expansion configuration by the *** to operational delays,the system will become unstable,and the service will be *** work proposed an RBACS that vigorously amended the distribution of containers operating in the entire Kubernetes *** allocation pattern is analyzed with the Kubernetes *** estimate the overall cost of RBACS,we use several scientific benchmarks comparing the accomplishment of container to remote node migration and on-site *** experiments ran on the simulations to show the method’s effectiveness yielded high precision in the real-time deployment of resources in eco *** to the default baseline,Kubernetes results in much fewer dropped requests with only slightly more supplied resources.

关键词： Docker container virtualization cloud resource kubernetes

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Progressive Stepwise Diffusion Model with Dual Decoders for Semi-Supervised Medical Image Segmentation

Progressive Stepwise Diffusion Model with Dual Decoders for ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Huang, Xiaolin Lin, Jingchun Chen, Jiacheng Ma, Xiao Chen, Bingzhi Lu, Guangming South China Normal University School of Software Foshan China Harbin Institute of Technology Department of Computer Science Shenzhen China

ISBN: (纸本)9798350386226

Semi-supervised medical image segmentation tasks aim to harness the potential of vast amounts of unlabeled data using a limited amount of annotated data. Denoising Diffusion Probabilistic Models, which have achieved significant success in image generation, are gradually being explored for their potential in semantic image segmentation. However, their application in semi-supervised medical image segmentation is still in its early stages. Initially, due to the high randomness of diffusion models, the pseudo-labels generated during the early training phase may mislead the processing of unlabeled data. Additionally, the use of fixed-time steps for random sampling during training limits the ability of the model to learn effective denoising functions at an early stage. To address these issues, we propose an innovative framework named Progressive Stepwise Diffusion Network with Dual Decoders (PSDD) for semi-supervised medical image segmentation. This framework incorporates an additional normal decoder into the denoising diffusion encoder-decoder structure to provide more accurate labels and employs a Progressive Incremental Step strategy to gradually train the model for longer generation processes. Evaluated on two 2D colon polyp segmentation datasets and a 3D Left Atrium dataset, the experimental results demonstrate significant performance improvements over current advanced methods, thereby validating the effectiveness and potential of this framework in handling complex semi-supervised learning scenarios. © 2024 IEEE.

关键词： Semantic Segmentation

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MA-MFIF: When misaligned multi-focus Image fusion meets deep homography estimation

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Multimedia Tools and Applications 2025年第12期84卷 10877-10898页

作者： Zhao, Baojun Luo, Fei Fuentes, Joel Ding, Weichao Gu, Chunhua School of Information Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Department of Computer Science and Information Technologies Universidad del Bio-Bio Chillán3780000 Chile

Multi-focus image fusion is a technique that combines multiple out-of-focus images to enhance the overall image quality. It has gained significant attention in recent years, thanks to the advancements in deep learning. However, one of the persistent challenges in this field is the processing of misaligned data, which can negatively impact the fusion results. To overcome this problem, a novel fusion framework with pre-registration is proposed for the fusion of misaligned multi-focus images. For pre-registration, content-aware deep homography estimation is used, which performs transfer learning on a real multi-focus image dataset to adapt to registration under defocused conditions. For fusion, a fusion module with dual-branch feature interaction is utilized to avoid invalid feature fusion and trained on real light field dataset to achieve better fusion performance. Qualitative and quantitative experimental results show that the proposed method has a 2-3 percentage point improvement in multiple evaluation metrics compared to existing advanced registration and fusion methods, and a maximum improvement of 4.83 percentage points in fusion performance when tested independently on the Lytro dataset. Additionally, We find that the value of the Qcv metric is greatly influenced by the alignment status of the input images, leading to its inability to reflect the fusion quality of aligned images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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Lung Cancer Analysis and Classification using Enhanced Efficient Net V3 6

Lung Cancer Analysis and Classification using Enhanced Effic...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Ramya, J. Poongodi, A. Department of Software Applications Agurchand Manmull Jain College Chennai India Department of Computer Sciences Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

Lung cancer is one of the main causes of death globally and adds to the burden of disease and mortality. Early detection of lung cancer may reduce the chance of developing lung cancer. Artificial intelligence (AI) in diagnostic imaging has garnered a lot of attention in recent years, particularly for the detection of lung cancer. On the other hand, manual CT scan analysis takes a lot of time and is inaccurate or prone to mistakes. Given these limitations, the accurate categorization of CT images as either malignant or non-cancerous is accelerated by the application of computer approaches, including machine learning and deep learning algorithms. In this work, we provide a new and improved CNN model architecture with FCN and Deeplab V3+ for segmentation and classification using hybrid efficient net B0 to investigate lung cancer. The suggested model achieves superior results in both segmentation and classification. © 2025 IEEE.

关键词： Lung cancer

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Service Level Agreement Monitoring as a Service: An Independent Monitoring Service for Service Level Agreements in Clouds

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Big Data 2023年第5期11卷 339-354页

作者： Badshah, Afzal Jalal, Ateeqa Farooq, Umar Rehman, Ghani-Ur Band, Shahab S. Iwendi, Celestine Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan Department of Computer Science University of Science and Technology Bannu Pakistan Department of Computer Science and Bioinformatics Khushal Khan Khattak University Karak Pakistan Future Technology Research Center College of Future National Yunlin University of Science and Technology Douliou Taiwan School of Creative Technologies University of Bolton Bolton United Kingdom

The cloud network is rapidly growing due to a massive increase in interconnected devices and the emergence of different technologies such as the Internet of things, fog computing, and artificial intelligence. In response, cloud computing needs reliable dealings among the service providers, brokers, and consumers. The existing cloud monitoring frameworks such as Amazon Cloud Watch, Paraleap Azure Watch, and Rack Space Cloud Kick work under the control of service providers. They work fine;however, this may create dissatisfaction among customers over Service Level Agreement (SLA) violations. Customers’ dissatisfaction may drastically reduce the businesses of service providers. To cope with the earlier mentioned issue and get in line with cloud philosophy, Monitoring as a Service (MaaS), completely independent in nature, is needed for observing and regulating the cloud businesses. However, the existing MaaS frameworks do not address the comprehensive SLA for customer satisfaction and penalties management. This article proposes a reliable framework for monitoring the provider’s services by adopting third-party monitoring services with clearcut SLA and penalties management. Since this framework monitors SLA as a cloud monitoring service, it is named as SLA-MaaS. On violations, it penalizes those who are found in breach of terms and condition enlisted in SLA. Simulation results confirmed that the proposed framework adequately satisfies the customers (as well as service providers). This helps in developing a trustworthy relationship among cloud partners and increases customer attention and retention. © Mary Ann Liebert, Inc.

关键词： Sales

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AI LipReader-Transcribing Speech from Lip Movements 6

AI LipReader-Transcribing Speech from Lip Movements

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ajitha, D. Dutta, Disha Saha, Falguni Giri, Parus Kant, Rohan School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Software Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Database Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Information Security Vellore India

ISBN: (纸本)9798350306613

Lip Reading AI is a discipline that is rapidly changing and has numerous applications in security, accessibility and human-computer interaction. This paper proposes a model which combines Convolutional Neural Networks (CNNs) to capture spatial capabilities, Long Short-Term Memory (LSTM) networks to examine temporal dependencies, and an adaptive interest mechanism. Meticulous preprocessing of the MIRACL VC-l dataset addressing challenges including one of a kind lip moves and occlusions accompanied with the aid of transitioning this study effortlessly to LRS2 dataset to complement lexemic versatility is one of its key function. The effects verify its robustness throughout unique datasets with superior overall performance towards cutting-edge techniques. Ablation checks suggest the crucial significance of every element in phrases of improving lip analyzing accuracy. Our proposed model version additionally suggests flexibility in restricted and naturalistic language situations. © 2024 IEEE.

关键词： Attention Mechanism Convolutional Neural Networks (CNNs) Deep Learning Lip Reading Long Short-Term Memory (LSTM) LRS2 Dataset MIRACLVC-J Dataset

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A Model for Detection of the Susceptible in Hospital Contact Network Based on Social Network Analysis 14th

A Model for Detection of the Susceptible in Hospital Contact...

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14th International Conference on computer Science and its Applications, CSA 2022 and the 16th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2022

作者： Yang, Yixuan Park, Doo-Soon Kim, Seok-Hoon Peng, Sony Siet, Sophort Ugli, Sadriddinov Ilkhomjon Rovshan Vilakone, Phonexay Department of Software Convergence Soonchunhyang University Asan Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan Korea Republic of Department of Computer Engineering and Information Technology National University of Laos Vientiane Laos

ISBN: (纸本)9789819912513

COVID-19 can be modeled as a SEIR disease transmission model, and early detection and prevention of susceptible is a great way to stop the spread of the virus. The purpose of this paper is to conduct community detection on the hospital's contact network (including patients, nurses, doctors, and managers), obtain frequently contacted communities (susceptible communities), and analyze the independent contact networks of different roles to obtain high-influence communities and rank the node influence, if nodes with high influence are included in the susceptible community, we will recommend focusing on observing these nodes to prevent the spread of virus. In this paper, we introduce a model that combines social networks analysis with disease transmission model to analyze hospital contact networks, apply the classic overlapping community detection method CPM to community detection of contact networks, and utilize PageRank algorithm to calculate the node's influence. The experimental results show that the model can effectively solve our problem. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hospitals

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