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Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： G. Pavithra M. Sindhu Sree T.C. Manjunath Manikandan M G. Dhivyasri Electronics and Communication Engineering Dayananda Sagar College of Engineering Bengaluru Karnataka India Electronics and Communication Engineering Presidency University Bengaluru Karnataka India Dept. of CSE (Data Science) Sai Vidya Institute of Technology Rajanakunte Bengaluru Karnataka India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

The paper introduces a comprehensive design and implementation framework for a bi-level power supply stepper motor drive interface, facilitating control of a five-axis robot arm through a computer’s parallel port. Additionally, it describes the development of simulation software used to control the robot arm for PNP (pick and place) operations, specifically for the precise placement of cylindrical metal casings containing uranium pellets into slots with welded small pads. The primary focus of the paper is on designing and implementing a bi-level stepper motor drive tailored for a five-axis robot manipulator. This system integrates a sophisticated stepper motor drive architecture with dual levels of control to improve the precision and efficiency of the robot manipulator’s movements. Advanced features, such as micro-stepping and precise motion control, are incorporated into the design to optimize the performance of the stepper motors. The dual-level control strategy aims to synchronize and coordinate the movements of the five-axis robot manipulator, ensuring smooth and accurate operation. Moreover, the paper discusses the development of specialized control algorithms to further enhance the performance of the stepper motors, enabling precise positioning and movement of the robot arm. These algorithms likely include trajectory planning to ensure efficient movement between points and collision avoidance. The simulation software developed for controlling the robot arm likely includes a user-friendly interface for programming and monitoring the robot’s movements. It may also feature simulation capabilities to test the robot’s movements before implementing them in the physical environment, reducing the risk of errors and optimizing the robot’s performance. Additionally, the paper may delve into the mechanical design considerations of the robot arm, such as the materials used, the range of motion of each axis, and any safety features implemented to protect the robot and its

关键词： Motor drives Uranium Power supplies Robot kinematics Welding Software algorithms Manipulators Motors Software Collision avoidance

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Multi-layer network modelling of genomic and transcriptomic data to investigate the response to checkpoint inhibitors in NSCLC

Multi-layer network modelling of genomic and transcriptomic ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Davide Mascolo Lorenzo Farina Manuela Petti MSc Program in Data Science Sapienza University of Rome Rome Italy Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

Recent innovations and developments in molecular biology and biotechnology have made it possible to acquire and store large omics datasets. In particular genomics, transcriptomics and the study of their relationship represent the key elements to understand how genotype influences phenotype. In this work we investigate the potential of multi-layer network modeling in integrating genomic and transcriptomic data of 152 advanced non-small cell lung cancer (NSCLC) patients treated with anti-PD-(L)1 therapy. For the transcriptomic layer, we performed differential expression and differential co-expression analyses in order to identify a subset of key genes in differentiating responder patients from non-responders. Adding to the genomic-transcriptomic model other three layers related to immune, myeloid and curated immunotherapy-based literature signatures we obtained a 5-layer Patient Similarity Network. The application of Similarity Network Fusion algorithm revealed a statistically significant stratification of patients (p-value ≤ 0.05) which allows the identification of two clusters characterized by patients responding and not responding to immunotherapy.

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Preserving Cluster Features in Imputing High Dimensional data with Extensive Missing Rate

Preserving Cluster Features in Imputing High Dimensional Dat...

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IEEE International Conference on Big data

作者： Chih Lai Carolin Poschen Lisa Maria Steinheuer Jörg Hackermüller Dept. Software & Data Science University of St. Thomas St. Paul MN USA Dept. Computational Biology Helmholtz Centre for Environmental Research / UFZ Leipzig Germany

ISBN: (纸本)9781665480468

It is a daunting task to impute a large dataset that has majority of data missing from its tens of thousands of predictors, but still to preserve a known cluster structure in the imputed results. In this study, we propose a novel two-step approach for this task. First, we use simple linear classification models to derive the global and local features of cluster structures from a template ground truth (i.e. known gold data). Second, we integrate the cluster features extracted from each cluster into a neural network architecture and its training responses for guiding the imputation process. Since our neural network utilizes the global and local features of gold data in training the imputation network, we refer our neural network as GLIN (Global-Local Imputation Network). We test our imputation method on two high-dimensional datasets: a single cell dataset and a movie rating dataset, that have up to of 95% missing rates. Finally, we use four evaluation metrics: distance, correlation, data distribution, and predictability difference, to evaluate how well the cluster structures of the gold data are preserved in the imputed results.

关键词： Training Measurement Gold computational modeling Microprocessors Neural networks Computer architecture

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Randomized Algorithms for Symmetric Nonnegative Matrix Factorization

arXiv

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arXiv 2024年

作者： Hayashi, Koby Aksoy, Sinan G. Ballard, Grey Park, Haesun School of Computational Science and Engineering Georgia Institute of Technology AtlantaGA United States Pacific Northwest National Laboratory SeattleWA United States Dept. of Computer Science Wake Forest University Winston-SalemNC United States

Symmetric Nonnegative Matrix Factorization (SymNMF) is a technique in data analysis and machine learning that approximates a symmetric matrix with a product of a nonnegative, low-rank matrix and its transpose. To design faster and more scalable algorithms for SymNMF we develop two randomized algorithms for its computation. The first algorithm uses randomized matrix sketching to compute an initial low-rank input matrix and proceeds to use this input to rapidly compute a SymNMF. The second algorithm uses randomized leverage score sampling to approximately solve constrained least squares problems. Many successful methods for SymNMF rely on (approximately) solving sequences of constrained least squares problems. We prove theoretically that leverage score sampling can approximately solve nonnegative least squares problems to a chosen accuracy with high probability. Finally we demonstrate that both methods work well in practice by applying them to graph clustering tasks on large real world data sets. These experiments show that our methods approximately maintain solution quality and achieve significant speed ups for both large dense and large sparse *** Codes 65F55, 65F20 Copyright © 2024, The Authors. All rights reserved.

关键词： Matrix algebra

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Grey Wolf Optimizer and Deep Neural Network based Feature Selection and Classification in Medical Image Analysis

Grey Wolf Optimizer and Deep Neural Network based Feature Se...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Katha Chandrashekhar P. Leela K. Reddy Madhavi J. Avanija Naresh Tangudu Computer Science and Engineering Indur Institute of Engineering and Technology Siddipet India Dept. of Data Science School of Computing Mohan Babu University Tirupati India School of Computing Mohan Babu University Tirupati India Aditya Institute of Technology and Management Tekkali India

Accurate and efficient medical image analysis is required for modern healthcare applications, facilitating timely disease diagnosis and treatment. Considering the importance of optimization algorithms and deep learning methodologies, this work presents a novel approach, Grey Wolf Optimizer-Assisted Deep Neural Networks (GWO-DNN), for feature selection and classification in medical image analysis. The proposed framework utilizes the power of the Grey Wolf Optimizer to intelligently select the most relevant features from complex medical images. The selected features are then integrated into Deep Neural Networks, to create accurate models for medical image classification tasks. This integrated approach is designed to improve diagnostic accuracy and enhance patient care. The experimentation and evaluation, reveal that the proposed GWO-DNN based model for image analysis is more efficient than the existing methods.

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Online Learning and Optimization for Queues with Unknown Demand Curve and Service Distribution

arXiv

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arXiv 2023年

作者： Chen, Xinyun Liu, Yunan Hong, Guiyu School of Data Science Chinese University of Hong Kong Shenzhen China Dept. of Industrial and Systems Engineering North Carolina State University United States

We investigate an optimization problem in a queueing system where the service provider selects the optimal service fee p and service capacity µ to maximize the cumulative expected profit (the service revenue minus the capacity cost and delay penalty). The conventional predict-then-optimize (PTO) approach takes two steps: first, it estimates the model parameters (e.g., arrival rate and service-time distribution) from data;second, it optimizes a model based on the estimated parameters. A major drawback of PTO is that its solution accuracy can often be highly sensitive to the parameter estimation errors because PTO is unable to properly link these errors (step 1) to the quality of the optimized solutions (step 2). To remedy this issue, we develop an online learning framework that automatically incorporates the aforementioned parameter estimation errors in the solution prescription process;it is an integrated method that can "learn" the optimal solution without needing to set up the parameter estimation as a separate step as in PTO. Effectiveness of our online learning approach is substantiated by (i) theoretical results including the algorithm convergence and analysis of the regret ("cost" to pay over time for the algorithm to learn the optimal policy), and (ii) engineering confirmation via simulation experiments of a variety of representative examples. We also provide careful comparisons for PTO and the online learning method. Copyright © 2023, The Authors. All rights reserved.

关键词： Parameter estimation

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Deep Learning Model for Intrusion Detection in SDN Networks

Deep Learning Model for Intrusion Detection in SDN Networks

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Optimization Techniques for Learning (ICOTL), International Conference on

作者： V. Jyothsna E. Sandhya Thammisetty Swetha P. Lokesh Kumar Reddy B. Jyothsna P. Bhasha Dept. of Data Science Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Department of CSE(Artificial Intelligence) Madanapalle Institute of Technology & Science Madanapalle India Department of CSE (Data Science) Madanapalle Institute of Technology & Science Madanapalle India Sri Venkateswara College of Engineering (Autonomous) Tirupati Department of CSE Viswam Engineering College Madanapalle Department of Data Science Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India

Network security has grown in importance as computer network technology has progressed. Today networking technologies are vulnerable to a variety of security risks, and intrusion detection has emerged as a key tool for identifying network attacks. The architecture of Software-Defined Networks (SDN) is utilized to offer network monitoring and function observation. In order to protect computer networks and systems, IDS serves a crucial role. In order to keep the SDN’s security at a high level, an IDS is typically created to monitor the SDN’s normal traffic. A SDN network’s different security threats can be detected and managed using a number of strategies. For the purpose to identify the attacks, we combine LSTM with a deep convolution neural network (Deep CNN) driven by Grey Wolf Optimization (GWO). Effective intrusion detection requires a CNN feature extraction procedure, deep LSTM is used to detect network intrusion, and a deep CNN-LSTM is trained with a specially devised optimization strategy to improve detection performance.

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Classification of Awareness in Disorders of Consciousness Using Common Spatial Pattern

Classification of Awareness in Disorders of Consciousness Us...

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International Winter Workshop on Brain-Computer Interface (BCI)

作者： Chaewon Lee Sunho Lee Jitka Annen Ji-Hoon Jeong Marcello Massimini Silvia Casarotto Melanie Boly Olivier Bodart Aurore Thibaut Mario Rosanova Steven Laureys Olivia Gosseries Minji Lee Dept. Data Science The Catholic University of Korea Bucheon Republic of Korea Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Republic of Korea Dept. GIGA-Research & Neurology University and University Hospital of Liege Liege Belgium Dept. Data Analysis University of Ghent Ghent Belgium Dept. Computer Science Chungbuk National University Cheongju Republic of Korea Dept. Biomedical and Clinical Sciences “L. Sacco” University of Milan Milan Italy Dept. Psychiatry Wisconsin Institute for Sleep and Consciousness University of Wisconsin Madison USA Coma Science Group GIGA-Consciousness GIGA Research Center University of Liège Liège Belgium Centre du Cerveau Neurology department University Hospital of Liège Liège Belgium

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Advances in intensive care have improved the survival rate of patients with severe acute brain injury, but diagnostic errors for patients with disorders of consciousness are still high. Accurate diagnosis of these patients is very important because effective treatment can vary depending on the diagnosis. In this study, we propose a framework for classifying unresponsive wakefulness syndrome and minimally conscious state, focusing on awareness. In particular, power spectral density and common spatial patterns were used together, considering that spatial information is a key feature in consciousness. The 16 patients with unresponsive wakefulness syndrome and 14 with minimally conscious state underwent resting-state electroencephalography measurements. In addition, we compared the performance by utilizing each frequency (delta, theta, alpha, beta, gamma bands) related to consciousness. As a result, the highest accuracy of 95.06% was achieved by the EEGNet classifier, especially in the beta frequency band. We demonstrated that spatial information is very important in consciousness, as we observed that classification performance improved when common spatial patterns were used. These results provide insight into various frameworks for diagnosing patients with disorders of consciousness and may help patients survive by increasing the diagnosis rate in the future.

关键词： Deep learning Accuracy Biological system modeling Pattern classification Focusing Signal processing Brain modeling Particle measurements Electroencephalography Robustness

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Observer study-based evaluation of TGAN architecture used to generate oncological PET images

Observer study-based evaluation of TGAN architecture used to...

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Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment

作者： Fedrigo, Roberto Yousefirizi, Fereshteh Liu, Ziping Jha, Abhinav K. Bergen, Robert V. Rajotte, Jean-Francois Ng, Raymond T. Bloise, Ingrid Harsini, Sara Kadrmas, Dan J. Uribe, Carlos Rahmim, Arman Dept. of Integrative Oncology BC Cancer Research Institute VancouverBC Canada Dept. of Physics and Astronomy Univ. of British Columbia VancouverBC Canada Dept. of Biomedical Engineering Washington Univ. in St. Louis St. LouisMO United States Mallinckrodt Institute of Radiology Washington Univ. School of Med. St. LouisMO United States Univ. of British Columbia Data Science Institute 6339 Stores Road VancouverBC Canada Dept. of Functional Imaging BC Cancer VancouverBC Canada Dept. of Radiology and Imaging Sciences University of Utah Salt Lake CityUT United States Dept. of Radiology Univ. of British Columbia VancouverBC Canada

ISBN: (数字)9781510660403

ISBN: (纸本)9781510660397

The application of computer-vision algorithms in medical imaging has increased rapidly in recent years. However, algorithm training is challenging due to limited sample sizes, lack of labeled samples, as well as privacy concerns regarding data sharing. To address these issues, we previously developed (Bergen et al. 2022) a synthetic PET dataset for Head & Neck (H&N) cancer using the temporal generative adversarial network (TGAN) architecture and evaluated its performance segmenting lesions and identifying radiomics features in synthesized images. In this work, a two-alternative forced-choice (2AFC) observer study was performed to quantitatively evaluate the ability of human observers to distinguish between real and synthesized oncological PET images. In the study eight trained readers, including two board-certified nuclear medicine physicians, read 170 real/synthetic image pairs presented as 2D-transaxial using a dedicated web app. For each image pair, the observer was asked to identify the "real"image and input their confidence level with a 5-point Likert scale. P-values were computed using the binomial test and Wilcoxon signed-rank test. A heat map was used to compare the response accuracy distribution for the signed-rank test. Response accuracy for all observers ranged from 36.2% [27.9-44.4] to 63.1% [54.8-71.3]. Six out of eight observers did not identify the real image with statistical significance, indicating that the synthetic dataset was reasonably representative of oncological PET images. Overall, this study adds validity to the realism of our simulated H&N cancer dataset, which may be implemented in the future to train AI algorithms while favoring patient confidentiality and privacy protection. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Network architecture

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Next Generation Intelligent Traffic Signal Control: Empowering Electronics Consumers with Edge-AIoT Capabilities

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IEEE Transactions on Consumer Electronics 2025年

作者： Chavhan, Suresh Doswada, Rohit Gupta, Deepak Gunjal, Saymam Rodrigues, Joel J.P.C. School of Data Sciences Indian Institute of Science Education and Research Kerala Thiruvananthapuram India C-DoT Karnataka Bangalore India Dept. of Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi India Centre for Research Impact and Outcome Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering University of Visvesvaraya College of Engineering Karnataka Bangalore India Federal University of Piaui PI Teresina Brazil

Traffic congestion has become a major issue that is being faced by the majority of road users. The increasing vehicle usage, and the lack of space and funds to construct new transport infrastructure, further complicates the issue. In this scenario, it is important to come up with an intelligent and economical solution that improves the quality of road users' service. The problem with the traffic handling framework is signal timings are fixed which is not adaptive to the density of vehicles. To address this issue we propose an Edge-Augument Artificial Intelligence of Things (AIoT) road user cooperation for traffic management. The proposed system efficiently utilizes electronic devices to learn and adapt to changing traffic conditions in real-time. By optimizing the traffic signal timings based on the actual traffic conditions, adaptive systems reduce delay, improve traffic flow, reduce fuel consumption and pollution, and improve the electronics consumers' and road users' experiences. The proposed system has been tested with real-time experiments by integrating Electronic devices like cameras, smartphones, and AGX Xavier (edge device) with Cloud (ThingSpeak). The proposed system is verified by simulating the proposed system in the SUMO traffic simulator and its reliability is concluded by comparing the waiting time, depart delay, running, and halt time with the existing traditional method. © 2025 IEEE.

关键词： Traffic congestion

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