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Decision making framework for heterogeneous QoS information: an application to cloud service selection

Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2915-2934页

作者： Tiwari, Rohit Kumar Kumar, Rakesh Baranwal, Gaurav Buyya, Rajkumar Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology Gorakhpur273010 India Department of Computer Science BHU Varanasi India Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Melbourne Australia

In recent times, appropriate decision-making in challenging and critical situations has been very well supported by multicriteria decision-making (MCDM) methods. The technique for order of preference by similarity to ideal solution (TOPSIS) is the most widely used MCDM method for solving decision problems. However, it restricts decision-makers to use only one type of Quality of Service (QoS) information, and it suffers from the rank reversal problem. Restriction to only one type of QoS makes the decision problems more challenging, as it restricts the decision-makers freedom. Further, the rank reversal problem makes the decision result unreliable. To address these issues of TOPSIS, we have proposed a reliable rank reversal robust modular TOPSIS (RMo-TOPSIS). RMo-TOPSIS allows crisp, interval, fuzzy, intuitionistic and neutrosophic fuzzy QoS metrics. It does not suffer from the rank reversal problem. Cloud computing provides computing services on-demand basis without involving maintenance by its users. The availability of many cloud service providers and their services makes cloud service selection a challenging problem. To validate RMo-TOPSIS, we select the cloud service selection consisting of different types of QoS metrics as an application. Experiments on cloud service selection show consistency and accuracy in results obtained by RMo-TOPSIS and its robustness against rank reversal. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision making

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Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

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computer Modeling in engineering & Sciences 2024年第8期140卷 1539-1562页

作者： Samar M.Alqhtani Toufique A.Soomro Faisal Bin Ubaid Ahmed Ali Muhammad Irfan Abdullah A.Asiri Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Electronic Engineering The University of LarkanoLarkanaSindh75660Pakistan Computer Science Department Sukkur IBA UniversitySukkurSindh65200Pakistan Eletrical Engineering Department Sukkur IBA UniversitySukkurSindh65200Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various *** resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain *** plays a crucial role in the diagnosis of brain tumors and the examination of other brain ***,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely ***,early diagnosis of brain tumors is intricate,necessitating the use of computerized *** research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain *** proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third *** final step involves classification using the Support Vector Machine(SVM)*** classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)*** proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of ***,this method exhibits a shorter processing time of 0.44 s compared to existing *** performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and *** enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classi

关键词： Brain tumor magnetic resonance imaging principal component analysis fuzzy c-clustering support vector machine

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Data-Driven Graph Construction of Power Flow Graphs for Electric Power Transmission Networks 23

Data-Driven Graph Construction of Power Flow Graphs for Elec...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Poole, Benjamin Ratnakumar, Rajan Madurasinghe, Dulip Tharaka Kummerle, Christian Venayagamoorthy, Ganesh Kumar Lee, Minwoo University of North Carolina Department of Computer Science Charlotte United States University of Pretoria Department of Electrical Electronic and Computer Engineering South Africa Clemson University Real-Time Power and Intelligent Systems Laboratory United States

ISBN: (纸本)9798350374889

A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specific methods, it lacks data-driven approaches that have proven effective in modeling the topology of complex systems. To address this gap, this paper explores the potential of data-driven methods for more accurate and adaptive solutions to uncover the true underlying topology of EPTNs. First, this paper examines Gaussian Graphical Models (GGM) to create an EPTN network graph (i.e., undirected simple graph). Second, to further refine and validate this estimated network graph, a physics-based, domain-specific refinement algorithm is proposed to prune false edges and construct the corresponding electric power flow network graph (i.e., directed multi-graph). The proposed method is tested using a synchrophasor dataset collected from a two-area, four-machine power system simulated on the real-time digital simulator (RTDS) platform. Experimental results show both the network and flow graphs can be reconstructed using various operating conditions and topologies with limited failure cases. © 2024 IEEE.

关键词： Undirected graphs

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Disentangling Spatio-Temporal Knowledge for Weakly Supervised Object Detection and Segmentation in Surgical Video

Disentangling Spatio-Temporal Knowledge for Weakly Supervise...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Liao, Guiqiu Jogan, Matjaz Koushik, Sai Eaton, Eric Hashimoto, Daniel A. University of Pennsylvania Pcaso Laboratory Department of Surgery United States University of Pennsylvania Department of Electrical and Systems Engineering United States University of Pennsylvania Department of Computer and Information Science United States

ISBN: (纸本)9798331510831

Weakly supervised video object segmentation (WSVOS) enables the identification of segmentation maps without requiring extensive annotations of object masks, relying instead on coarse video labels indicating object presence. WSVOS in surgical videos is, however, more challenging due to the complex interaction of multiple transient objects, such as surgical tools moving in and out of the surgical field. In this scenario, state-of-the-art WSVOS methods struggle to learn accurate segmentation maps. We address this problem by introducing ViDeo Spatio-Temporal disentanglement Networks (VDST-Net), a framework to disentangle complex spatio-temporal object interactions using semi-decoupled knowledge distillation to predict high-quality class activation maps (CAMs). A teacher network is designed to help a temporal-reasoning student network resolve activation conflicts, as the student leverages temporal dependencies when specifics about object location and timing in the video are not provided. We demonstrate the efficacy of our framework on a challenging surgical video dataset where objects are, on average, present in less than 60% of annotated frames, and compare our method to state-of-the-art methods on surgical data and on a public dataset commonly used to benchmark WSVOS. Our method outperforms state-of-the-art techniques and generates accurate segmentation masks under video-level weak supervision. Our code is available at: https://***/PCASOlab/VDST-net. © 2025 IEEE.

关键词： Image segmentation

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Medibot-Healthcare Robot Assistant for Remote Communities 20

Medibot-Healthcare Robot Assistant for Remote Communities

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Chakma, Andrea Noel, Emile University of Waterloo Department of Systems Design Engineering Waterloo Canada University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada

ISBN: (纸本)9798350378931

Across the world, the lack of proper healthcare resources in rural areas significantly impacts the physical and economic well-being of individuals living there. There is a need for multi-faceted solutions to help alleviate healthcare issues in these areas. Medibot is a robot which monitors the health of individuals in these areas and takes appropriate measures. Patients visit Medibot for daily non-invasive monitoring of their vitals and then receive updated guidance or are connected with a healthcare professional for immediate support. The robot provides an easy way to get continuous care for those with limited access to healthcare resources or facilities. © 2025 IEEE.

关键词： Nutrition

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A 4.5 μW Miniaturized 3-Channel Wireless Intra-Cardiac Acquisition System

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IEEE Transactions on Biomedical Circuits and systems 2023年第5期17卷 1097-1110页

作者： Rezaeiyan, Yasser Koolivand, Yarallah Zamani, Milad Shoaei, Omid Akbari, Meysam Moradi, Farshad Tang, Kea-Tiong Integrated Circuits and Electronics Laboratory Department of Engineering Aarhus University Aarhus8200 Denmark Electrical Engineering KN Toosi University of Technology Tehran15418-49611 Iran Bio-Integrated Systems Lab School of Electrical and Computer Engineering University of Tehran Tehran*** Iran Department of Electrical Engineering University of Kurdistan Sanandaj*** Iran Department of Electrical Engineering National TsingHua University Hsinchu30013 Taiwan

This article presents a chip designed for wireless intra-cardiac monitoring systems. The design consists of a three-channel analog front-end, a pulse-width modulator featuring output-frequency offset and temperature calibration, and inductive data telemetry. By employing a resistance boosting technique in the instrumentation amplifier feedback, the pseudo-resistor exhibits lower non-linearity, leading to a total harmonic distortion of below 0.1%. Furthermore, the boosting technique enhances the feedback resistance, leading to a reduction in the size of the feedback capacitor and, consequently, the overall size. To make the modulator's output frequency resilient to temperature and process changes, coarse and fine-tuning algorithms are used. The front-end channel is capable of extracting the intra-cardiac signal with an effective number of bits of 8.9, while exhibiting an input-referred noise of less than 2.7 μ Vrms, and consuming 200 nW per channel. The front-end output is encoded by an ASK-PWM modulator, which drives an on-chip transmitter at 13.56 MHz. The proposed System-on-Chip (SoC) is fabricated in a 0.18 μ m standard CMOS technology and consumes 4.5 μW while occupying 1.125 mm2. © 2023 IEEE.

关键词： Transmitters

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Drowning Recognition for Ocean Surveillance using computer Vision and Drone Control

Drowning Recognition for Ocean Surveillance using Computer V...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Rakotondraibe, Maureen Fang, Tianyang Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

According to WHO's report from 2021, Drowning is the 3rd leading cause of unintentional death worldwide. The use of autonomous drones for drowning recognition can increase the survival rate and help lifeguards and rescuers with their life saving mission. This paper presents a real-time drowning recognition model and algorithm for ocean surveillance that can be implemented on a drone. The presented model has been trained using two different approaches and has 88% accuracy. Compared to the contemporary models of drowning recognition designed for swimming pools, the model presented is better suited for outdoor applications in the ocean. © 2023 IEEE.

关键词： Drones

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Utilizing computer Vision Algorithms to Detect and Classify Cyberattacks in IoT Environments in Real-Time

Utilizing Computer Vision Algorithms to Detect and Classify ...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Gromov, Mikhail Arnold, David Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

computer vision has proven itself capable of accurately detecting and classifying objects within images. This also works in cases where images are used as a way of representing data, without being actual photographs. In cybersecurity, computer vision is rarely used, however it has been used to detect botnets successfully. We applied computer vision to determine how well it would be able to detect and classify a large number of attacks and determined that it would be able to run at a decent rate on a Jetson Nano. This was accomplished by training a convolutional neural network using data publicly available in the IoT-23 database, which contains packet captures of IoT devices with and without different malware infections. The neural network was evaluated on an RTX 3050 and a Jetson Nano to see if it could be used in IoT. © 2023 IEEE.

关键词： computer vision

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Application of Machine Learning and Image Recognition for Driver Attention Monitoring

Application of Machine Learning and Image Recognition for Dr...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Tailor, Manav Ali, Jahangir Yu, Xinrui Yi, Won-Jae Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

This paper presents a real-time prototype system for monitoring the distraction levels of the driver. Due to the nature of high traffic conditions commonly seen nowadays, accidents are highly likely to occur as drivers cannot always recognize their exhaustion levels themselves. We utilize a single low-cost camera facing the driver connected to a single-board computer;a series of frame captures from the camera are fed to a neural network, and a pattern detection algorithm to predict the driver's distraction level is utilized. All training is conducted under personalized training sets to increase accuracy and to match an individual's driving patterns as accurately as possible. This system is designed to serve as a baseline for further system development, and many vital sub-components can be changed regarding input data type and choices of machine learning algorithms. © 2023 IEEE.

关键词： Cameras

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Data Compression for Ultrasonic Microstructure Scattering Signals using Unsupervised Neural Networks

Data Compression for Ultrasonic Microstructure Scattering Si...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Zhang, Xin Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9798350346459

Ultrasonic Nondestructive Evaluation (NDE) has been extensively used to characterize the microstructure of metallic structures for early exposure of materials integrity. However, industrial NDE requires the processing, storage, and real-time transmission of large volumes of ultrasonic data. Therefore, it is indispensable to compress ultrasonic data with high fidelity. In this study, we explore the development of Unsupervised Learning (UL) based Neural Network (NN) models for massive ultrasonic data compression and an innovative multilayer perceptron residual autoencoder: Ultrasonic Residual Compressive Autoencoder (URCA), is introduced to compress ultrasonic data with high compression performance. This URCA can be fast-trained and utilizes the sparsity penalty with residual connection to optimize compression performance. UL-based NNs allow for memory-efficient training and rapid online augmentation of the model. To examine the results, a high-performance ultrasonic signal acquisition system was assembled to automatically collect ultrasonic data from heat-treated 1,018 steel blocks for microstructure characterization. Compression performance is analyzed based on compression ratio, reconstruction accuracy and model training time. The reconstruction accuracy was measured using the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR). By training the URCA NN for a high reconstruction performance of 0.96 SSIM, we obtained 91.25% memory space-saving. For a higher compression performance of 0.80 SSIM, we obtained 96.04% memory space-saving. © 2023 IEEE.

关键词： Data compression

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