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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Abhijit Kumar Sumit Kumar Kevin Riou Amitaya Choudhury Gurinder Singh Sudhanshu Tripathi School of Computer Science University of Petroleum and Energy Studies Dehradun India Thapar Institute of Engineering and Technology Patiala Punjab India Polytech Nantes University of Nantes France Pundit Deendayal Energy University Ahmadabad Gujrat India Group Vice-Chancellor Amity University Uttar Pradesh India Department of IT and Engineering Amity University Tashkent Uzbekistan

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Classifying breast cancer into metastatic non-metastatic is crucial for a women that undergoing for surgery, because of the heavy treatment associated with metastatic cancers. With the advancement of micro array technology, identifying gene signatures from large genes dataset becomes an efficient approach to predict or classify cancerous genes. We proposed an algorithm based on support vector machine using enhanced recursive feature extraction mechanism (SVM_ ERFE). The proposed algorithm identifies most significant genes signatures and thus prognosis of metastatic/relapsing nature among breast cancer patients. In order to enhance the accuracy, we kept allow to varying the number of genes after first filtering. Several experiments has performed over metastatic-295 breast cancer patient's datasets and relaps-GSE2034 dataset, and achieved the best accuracy 100%±0.0 for 158 and 307 gene signatures after first filtering respectively. Further, the model has validated using 10-fold cross validation using evaluation metrics accuracy, TPR, TNR. Moreover, proposed model can increase the dortor's confidence into the results.

关键词： Support vector machines Accuracy Filtering Computational modeling Feature extraction Prediction algorithms Breast cancer Vectors Classification algorithms Prognostics and health management

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DNA serial dilution: an investigation toward accurate broadband plasmonic signature 22

DNA serial dilution: an investigation toward accurate broadb...

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Plasmonics in Biology and Medicine XXII 2025

作者： Dehzangi, Arash Ghasemi, Masih Choudhury, P.K. Department of Electrical and Computer Engineering University of Texas at Dallas RichardsonTX United States Laser and Plasma Research Institute Shahid Beheshti University Daneshju Blvd.Evin Tehran Iran College of Optical Science and Engineering International Research Center for Advanced Photonics Zhejiang University Building 1A 718 East Haizhou Rd. Zhejiang Haining China

ISBN: (纸本)9781510684225

Serial dilution of Balb/c rat DNA in the visible broadband plasmonic spectroscopy was explored as a promising technique to reduce this genomic substance concentration in the nanoscale. The physical DNA of a living creature, after extraction from the resources (such as blood, nail, hair, tissue, and other possible organs) assumes amorphous pile of strands in the microscale or larger. The serial dilution technique was exploited to exponentially decrease the DNA concentration of BALB/c rat - the target living mammal - in the nanoscale, and the diluted DNA was deposited over a thin film of gold (for fixturing and removing moisture). Plasmonic surface waves of different frequencies were found to promote wave propagation more efficiently when lower thickness of unstructured-nanoscale-DNA layer was deposited (over gold thin film). The results manifest that the most diluted DNA sample reveals distinguishable plasmonic conditions. The sensitivity of the configuration was enhanced in the presence of a DNA sample (as compared to the case of non-existing measurand), which was further increased for larger incidence angles. © 2025 SPIE.

关键词： Surface plasmon resonance

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Telemedicine platform for health assessment remotely by an integrated nanoarchitectonics FePS_(3)/rGO and Ti_(3)C_(2)-based wearable device

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npj Flexible Electronics 2022年第1期6卷 735-744页

作者： Jayraj V.Vaghasiya Carmen C.Mayorga-Martinez Martin Pumera Center for Advanced Functional Nanorobots Department of Inorganic ChemistryFaculty of Chemical TechnologyUniversity of Chemistry and Technology PragueTechnická516628 PragueCzech Republic Energy Research Institute@NTU(ERI@N) Research Techno PlazaX-Frontier BlockLevel 550 Nanyang Drive637553 SingaporeSingapore Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava17.listopadu 2172/1570800 OstravaCzech Republic Department of Medical Research China MedicalUniversity HospitalChina Medical UniversityNo.91 Hsueh-Shih RoadTaichung 40402Taiwan

Due to the emergence of various new infectious(viral/bacteria)diseases,the remote surveillance of infected persons has become most important,especially if hospitals need to isolate infected patients to prevent the spreading of pathogens to health care ***,we develop a remote health monitoring system by integrating a stretchable asymmetric supercapacitor(SASC)as a portable power source with sensors that can monitor the human physical health condition in real-time and *** abnormal body temperature and breathing rate could indicate a person’s sickness/infection *** we integrated FePS3@graphene-based strain sensor and SASC into an all-in-one textile system and wrapped it around the abdomen to continuously monitor the breathing cycle of the *** real body temperature was recorded by integrating the temperature sensor with the *** proposed system recorded physiological parameters in real-time and when monitored remotely could be employed as a screening tool for monitoring pathogen infection status.

关键词： integrating wear portable

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MULTIMODAL SELF-SUPERVISED LEARNING FOR LESION LOCALIZATION

arXiv

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arXiv 2024年

作者： Yang, Hao Zhou, Hong-Yu Li, Cheng Huang, Weijian Liu, Jiarun Liang, Yong Shi, Guangming Zheng, Hairong Liu, Qiegen Wang, Shanshan Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Peng Cheng Laboratory Shenzhen China University of Chinese Academy of Sciences Beijing China Department of Computer Science The University of Hong Kong Pok Fu Lam Hong Kong Guangzhou China Department of Electronic Information Engineering Nanchang University China

Multimodal deep learning utilizing imaging and diagnostic reports has made impressive progress in the field of medical imaging diagnostics, demonstrating a particularly strong capability for auxiliary diagnosis in cases where sufficient annotation information is lacking. Nonetheless, localizing diseases accurately without detailed positional annotations remains a challenge. Although existing methods have attempted to utilize local information to achieve fine-grained semantic alignment, their capability in extracting the fine-grained semantics of the comprehensive context within reports is limited. To address this problem, a new method is introduced that takes full sentences from textual reports as the basic units for local semantic alignment. This approach combines chest Xray images with their corresponding textual reports, performing contrastive learning at both global and local levels. The leading results obtained by this method on multiple datasets confirm its efficacy in the task of lesion localization. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Exponential quantum speedup in simulating coupled classical oscillators*

Exponential quantum speedup in simulating coupled classical ...

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Annual IEEE Symposium on Foundations of computer science

作者： Ryan Babbush Dominic W. Berry Robin Kothari Rolando D. Somma Nathan Wiebe Google Quantum AI Venice CA United States School of Mathematical and Physical Sciences Macquarie University Sydney NSW Australia Department of Computer Science University of Toronto Toronto ON Canada Pacific Northwest National Laboratory Richland WA United States Canadian Institute for Advanced Research Toronto ON Canada

We study the problem of simulating the time evolution of a system of 2 n classical coupled oscillators (e.g., 2 n balls connected by springs) on a quantum computer. We map Newton’s equation for harmonic potentials to Schrödinger’s equation, such that the amplitudes of an $\mathcal{O}(n)$-qubit quantum state encode the momenta and displacements of the 2 n classical oscillators. Given oracle access to the masses and spring constants, we describe a quantum algorithm with query and time complexity poly (n) that solves this problem when certain parameters are polynomially bounded and the initial state is easy to prepare. As an example application, we apply our quantum algorithm to efficiently estimate the normalized kinetic energy of an oscillator at any time. We then show that any classical algorithm solving the same problem must make $2^{\Omega(n)}$ queries to the oracle and we also show that when the oracles are instantiated by poly (n)-size circuits, the problem is BQP-complete. Thus, our approach solves a potentially practical application with an exponential speedup over classical computers.

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Automating gamification personalization: To the user and beyond

arXiv

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arXiv 2021年

作者： Rodrigues, Luiz Toda, Armando M. Oliveira, Wilk Palomino, Paula T. Vassileva, Julita Isotani, Seiji Laboratory of Applied Computing to Education and Advanced Social Technology Institute of Mathematics and Computer Science University of São Paulo São Carlos Brazil Lab Department of Computer Science University of Saskatchewan Saskatoon Canada

Personalized gamification explores knowledge about the users to tailor gamification designs to improve one-size-fits-all gamification. The tailoring process should simultaneously consider user and contextual characteristics (e.g., activity to be done and geographic location), which leads to several occasions to tailor. Consequently, tools for automating gamification personalization are needed. The problems that emerge are that which of those characteristics are relevant and how to do such tailoring are open questions, and that the required automating tools are lacking. We tackled these problems in two steps. First, we conducted an exploratory study, collecting participants’ opinions on the game elements they consider the most useful for different learning activity types (LAT) via survey. Then, we modeled opinions through conditional decision trees to address the aforementioned tailoring process. Second, as a product from the first step, we implemented a recommender system that suggests personalized gamification designs (which game elements to use), addressing the problem of automating gamification personalization. Our findings i) present empirical evidence that LAT, geographic locations, and other user characteristics affect users’ preferences, ii) enable defining gamification designs tailored to user and contextual features simultaneously, and iii) provide technological aid for those interested in designing personalized gamification. The main implications are that demographics, game-related characteristics, geographic location, and LAT to be done, as well as the interaction between different kinds of information (user and contextual characteristics), should be considered in defining gamification designs and that personalizing gamification designs can be improved with aid from our recommender system. © 2021, CC BY.

关键词： Recommender systems

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Cognitive Improvement Estimation using EEG Imaging after tDCS Therapy

Cognitive Improvement Estimation using EEG Imaging after tDC...

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Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), International Conference on

作者： Mudassar Ayub Kaleem Ullah Muhammad Jawad Khan Hassan Farooq Afzaal Khan U.S. - Pakistan Centers for Advanced Studies in Energy (USPCAS-E) National University of Science & Technology (NUST) Islamabad Pakistan School of Electrical Engineering and Computer Science National University of Science & Technology (NUST) Islamabad Pakistan Department of robotics and artificial intelligence SMME National University of Science & Technology (NUST) Islamabad Pakistan

ISBN: (纸本)9781665492430

Brain is the most important organ in the human body. The effects of brain impairment are wide ranging and include cognition, fatigue, sleep issues, headaches, dizziness, impaired self-awareness, clinical depression, attention and concentration problems, epilepsy, struggle in making decisions and many more. The objective of this paper is to the efficacy of noninvasive neuro modulatory technique Transcranial direct current stimulation (tDCS) in the estimation of brain cognitive state improvement using changes in small electrical brain voltages recorded by Electroencephalogram (EEG). Event related De synchronization (ERDs) is applied to the Motor Imagery Period (MIP) and Rest Period (RP) of pre stimulation and post stimulation EEG data of ten subjects. Common Spatial Pattern (CSP) is used for feature extraction and Linear Discrimination Analysis (LDA) a machine learning model is applied on these features for classification. The results suggest a decrease of contralateral ERDs oscillatory activity related to an event as per the hypothesis in anodal post stimulation than pre stimulation across all channels for six subjects. Further LDA applied on CSP proved that the classification accuracy between Motor imagery period (MIP) and the Rest period (RP) after the stimulation therapy is higher than the Pre stimulation Motor Imagery period (MIP) and Rest period (RP).

关键词： Analytical models Medical treatment Estimation Machine learning DC motors Feature extraction Brain modeling

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A method for estimating reflectance map and material using deep learning with synthetic dataset

arXiv

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arXiv 2020年

作者： Lim, Mingi Yoon, Sung-Eui Department of Computer Science Korea Advanced Institute of Science and Technology

The process of decomposing target images into their internal properties is a difficult task due to the inherent ill-posed nature of the problem. The lack of data required to train a network is a one of the reasons why the decomposing appearance task is difficult. In this paper, we propose a deep learning-based reflectance map prediction system for material estimation of target objects in the image, so as to alleviate the ill-posed problem that occurs in this image decomposition operation. We also propose a network architecture for Bidirectional Reflectance Distribution Function (BRDF) parameter estimation, environment map estimation. We also use synthetic data to solve the lack of data problems. We get out of the previously proposed Deep Learning-based network architecture for reflectance map, and we newly propose to use conditional Generative Adversarial Network (cGAN) structures for estimating the reflectance map, which enables better results in many applications. To improve the efficiency of learning in this structure, we newly utilized the loss function using the normal map of the target object. Copyright © 2020, The Authors. All rights reserved.

关键词： Network architecture

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Revolutionizing Urban Drainage: A Smart IoT Approach to Stormwater Management Using AdaBoosting Algorithm

Revolutionizing Urban Drainage: A Smart IoT Approach to Stor...

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Advances in Modern Age Technologies for Health and Engineering science (AMATHE), International Conference on

作者： P S Thanigaivelu A. Sairam P. Vijayan A. Suresh Kumar N. Mohankumar T. R. GaneshBabu Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamil Nadu India Department of Management Studies Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

The management of stormwater in urban areas across the globe is becoming an increasingly difficult problem because of the increased risk of floods caused by climate change. Typical drainage methods often turn out to be insufficient, which calls for the development of novel approaches. This study suggests a smart stormwater management system powered by the Internet of Things and aims to boost urban resilience and reduce floods. The system continually monitors critical drainage characteristics, such as water levels, flow rates, and water quality, thanks to a network of Internet of Things sensors. To accurately predict probable flooding occurrences, advanced predictive algorithms examine this data together with incoming meteorological information. The technology performs real-time dynamic management of the drainage network, improving the efficiency of pumps and valves to reduce the likelihood of flooding. When potential flood hazards are identified, those who need to be informed, such as local authorities and citizens, are notified through an early warning system. The findings of this study lead to a more effective allocation of resources, an improvement in water quality, and an increase in the resilience of metropolitan areas to severe weather events. This approach, powered by the Internet of Things, promises a route toward sustainable and flood-resilient communities by transforming urban drainage.

关键词： Ethics Urban areas Water quality Prediction algorithms Real-time systems Stormwater Floods

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Characterizing Low-Frequency Qubit Noise

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Physical Review Applied 2023年第6期19卷 064066-064066页

作者： Filip Wudarski Yaxing Zhang Alexander N. Korotkov A.G. Petukhov M.I. Dykman Quantum Artificial Intelligence Laboratory Exploration Technology Directorate NASA Ames Research Center Moffett Field California 94035 USA USRA Research Institute for Advanced Computer Science (RIACS) Mountain View California 94043 USA Google Research Mountain View California 94043 USA Department of Electrical Engineering University of California Riverside California 92521 Department of Physics and Astronomy Michigan State University East Lansing Michigan 48824 USA

Fluctuations of the qubit frequencies are one of the major problems to overcome on the way to scalable quantum computers. Of particular importance are fluctuations with a correlation time that exceeds the decoherence time due to decay and dephasing by fast processes. The statistics of the fluctuations can be characterized by measuring the correlators of the outcomes of periodically repeated Ramsey measurements. We describe qubit dynamics during repeated measurements and evaluate the two-time correlator for noise from two-level systems and two- and three-time correlators for Gaussian noise. The explicit expressions for the correlators are compared with simulations. We find that, even though the three-time correlators for noise from two-level systems and for Gaussian noise are generally significantly different, already ten two-level systems can mimic Gaussian noise provided they are symmetric. This is not the case for asymmetric systems.

关键词： Quantum computation Quantum fluctuations & noise Quantum information with solid state qubits Quantum stochastic processes Superconducting qubits Monte Carlo methods Quantum master equation Two-level models

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