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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Gannamani Deepthi A.M Anusha Bamini Yenumala Joseph Praveen Division of Computer Science and Engineering Karunya Institute of Technology and Sciences

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This study explores the transformative potential of image classification algorithms like VGG16, ResNet, and DenseNet, for the early detection of pancreatic tumors using medical imaging. One of the main causes of cancer-related deaths globally is pancreatic cancer. However, pancreatic cancer detected at early stages can be cured. This study navigates through the evolution of CNNs in medical image analysis and their specific applications in pancreatic cancer detection. Pancreatic cancer poses a significant global health challenge due to its high mortality rates, often attributed to late-stage detection. The primary objective is to enhance diagnostic accuracy and enable timely intervention. The obtained results demonstrated promising accuracy levels for each model. VGG16 achieved an accuracy of 96%, ResNet demonstrated an accuracy of 98.31%, and DenseNet showcased an accuracy of 99%. Precision values for VGG16, ResNet, and DenseNet were 95%, 96%, and 95.2%, respectively. Recall values were 98% for VGG16, 95% for ResNet, and 97% for DenseNet. The F1 score for 98% for VGG16, 97% for ResNet, and 98.1% for DenseNet. The study concludes that VGG16, ResNet, and DenseNet are valuable tools for pancreatic tumor detection, with each architecture exhibiting unique advantages. By aiding in the early detection of pancreatic cancer, the suggested models may enhance patient outcomes. The research provides insights into the comparative performance of these deep learning architectures, guiding future developments in medical image analysis.

关键词： Accuracy Navigation Computational modeling Pancreatic cancer Deep architecture computer architecture Classification algorithms

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Differentially Private Online Federated Learning with Correlated Noise 63

Differentially Private Online Federated Learning with Correl...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Jiaojiao Zhu, Linglingzhi Johansson, Mikael Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science StockholmSE-100 44 Sweden Chinese University of Hong Kong Department of Systems Engineering and Engineering Management Hong Kong

ISBN: (纸本)9798350316339

We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local updates with streaming non-iid data, we develop a perturbed iterate analysis to control the impact of the DP noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed under a quasi-strong convexity condition. Subject to an (, δ) DP budget, we establish a dynamic regret bound over the entire time horizon, quantifying the impact of key parameters and the intensity of changes in dynamic environments. Numerical experiments confirm the efficacy of the proposed algorithm. © 2024 IEEE.

关键词： Federated learning

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A Confirmation Based Accident Detection System Using IoT for Smart Vehicles

A Confirmation Based Accident Detection System Using IoT for...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Bettina Ninan Division of Computer Science and Engineering Karunya Institute of Technology and Sciences

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

Accidents on the road continue to pose a significant threat to life and safety, necessitating innovative solutions to improve emergency response and minimize injuries. The proposed approach introduces an IoT-based Accident Detection System that integrates seamlessly with vehicle airbags, a confirmation button, GPS tracking, SOS calls, and a beeping system. The system harnesses a network of sensors to detect accidents accurately. When an accident occurs, it automatically deploys airbags to protect the passengers in the vehicle. A critical feature in the system is the manual confirmation button, which lets the occupants confirm the severity of the accident. If no confirmation is acquired within a set timeframe, the system proceeds to the next steps to save the occupant. The system uses GPS tracking with an accuracy of 96% to determine the location of the accident. Simultaneously, it initiates SOS calls to nearby hospitals and emergency services, ensuring seamless medical assistance. In addition, the system emits alerts to caution passersby and authorities about the *** system enhances emergency response, reducing the time between an accident occurring and medical attention. It not only minimizes injuries but also improves overall road safety. The proposed system aims to provide a brief overview of its multi-faceted capabilities, emphasizing its potential to save lives and reduce the severity of injuries in road accidents.

关键词： Micromechanical devices Accuracy Emergency services Sensor systems Road safety Sensors Injuries Accidents Global Positioning System Vehicles

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Hybrid Deep Learning and Ensemble Methods for Alzheimer’s Detection from EEG Signals 3

Hybrid Deep Learning and Ensemble Methods for Alzheimer’s D...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Anjali, Mokara Sundar, G. Naveen Meghana, Yetukuri gana bala Sreeja, Beereddy Narmadha Thangavel, Senthil Kumar Karunya Institute of technology and sciences Coimbatore India Division of AIML Karunya Institute of technology and sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798331532420

Alzheimer's disease (AD) is a progressive neurological disorder characterised by aberrant behaviour, memory loss, and cognitive impairment. Electroencephalography (EEG) is an efficient method that provides valuable information on brain activity and can be used to predict AD. The application of EEG data for AD classification is examined in this abstract, with particular attention paid to the study of frequency bands, and Machine Learning (ML) *** algorithms classifies AD with excellent levels of accuracy, indicating possibility of EEG-based methods for early disease identification. In order to classify AD in its early stages, this research proposes a unique deep learning model based on convolutional neural networks (CNNs) and signal processing techniques. The proposed model aims to classify patients into two categories: AD positive and negative. At the early stage the dataset was preprocessed using techniques such as label encoding and noise removal. Empirical mode decomposition (EMD) and variational mode decomposition (VMD) are the techniques applied in signal processing. The classification is done using Deep Learning (DL) and Machine Learning (ML) methods are used to develop the best model. ML algorithms namely Stacking classifier, Gradient Boosting, Stacknet, AdaBoost, and DL algorithms like Convolutional Neural Networks (CNN) and Deep Neural Network (DNN) architectures have shown promise in AD classification. Random Forest and AdaBoost excel in ensemble learning, while CNN and DNN models capture intricate patterns in EEG data, further enhancing the accuracy of diagnosis. In conclusion, EEG data offer non-invasive and economical method for classification of AD and contain useful information. © 2024 IEEE.

关键词： Convolutional neural networks

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Efficient quantum circuits for machine learning activation functions including constant T-depth ReLU

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Physical Review Research 2024年第4期6卷 043048页

作者： Wei Zi Siyi Wang Hyunji Kim Xiaoming Sun Anupam Chattopadhyay Patrick Rebentrost State Key Lab of Processors School of Computer Science and Technology School of Computer Science and Engineering Division of IT Convergence Engineering School of Computing

In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers. Among the components in this domain, activation functions hold a fundamental and indispensable role. Our research focuses on the development of activation functions quantum circuits for integration into fault-tolerant quantum computing architectures, with an emphasis on minimizing T-depth. Specifically, we present novel implementations of ReLU and leaky ReLU activation functions, achieving constant T-depths of 4 and 8, respectively. Leveraging quantum lookup tables, we extend our exploration to other activation functions such as the sigmoid. This approach enables us to customize precision and T-depth by adjusting the number of qubits, making our results more adaptable to various application scenarios. This study represents a significant advancement towards enhancing the practicality and application of quantum machine learning.

关键词： Machine learning Quantum algorithms Quantum algorithms & computation Quantum circuits Quantum computation

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A machine learning-guided design and manufacturing of wearable nanofibrous acoustic energy harvesters

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Nano Research 2024年第10期17卷 9181-9192页

作者： Negar Hosseinzadeh Kouchehbaghi Maryam Yousefzadeh Aliakbar Gharehaghaji Safoora Khosravi Danial Khorsandi Reihaneh Haghniaz Ke Cao Mehmet R.Dokmeci Mohammad Rostami Ali Khademhosseini Yangzhi Zhu Terasaki Institute for Biomedical Innovation Los AngelesCA 91367USA Department of Textile Engineering Amirkabir University of Technology(Tehran Polytechnic)Hafez Avenue*** TehranIran Department of Electrical and Computer Engineering University of British ColumbiaVancouverBC V6T1Z4Canada Chemical Sciences Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science University of Southern CaliforniaLos AngelesCA 90007USA

Nanofibrous acoustic energy harvesters(NAEHs)have emerged as promising wearable platforms for efficient noise-to-electricity conversion in distributed power energy systems and wearable sound amplifiers for assistive listening ***,their reallife efficacy is hampered by low power output,particularly in the low-frequency range(<1 kHz).This study introduces a novel approach to enhance the performance of NAEHs by applying machine learning(ML)techniques to guide the synthesis of electrospun polyvinylidene fluoride(PVDF)/polyurethane(PU)nanofibers,optimizing their application in wearable *** use a feed-forward neural network along with solving an optimization problem to find the optimal input values of the electrospinning(applied voltage,nozzle-collector distance,electrospinning time,and drum rotation speed)to generate maximum output performance(acoustic-to-electricity conversion efficiency).We first prepared a dataset to train the network to predict the output power given the input variables with high *** introducing the neural network,we fix the network and then solve an optimization problem using a genetic algorithm to search for the input values that lead to the maximum energy harvesting *** ML-guided wearable PVDF/PU NAEH platform can deliver a maximal acoustoelectric power density output of 829μW/cm^(3) within the surrounding noise *** addition,our system can function stably in a broad frequency(0.1-2 kHz)with a high energy conversion efficiency of 66%.Sound recognition analysis reveals a robust correlation exceeding 0.85 among lexically akin terms with varying sound intensities,contrasting with a diminished correlation below 0.27 for words with disparate semantic ***,this work provides a previously unexplored route to utilize ML in advancing wearable NAEHs with excellent practicability.

关键词： wearable electronics acoustic energy harvester machine learning piezoelectric nanogenerator electrospun nanofiber

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Deep learning analysis of smart meter data from a small sample of room air conditioners facilitates routine assessment of their operational efficiency

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Energy and AI 2024年第2期16卷 42-52页

作者： Weiqi Wang Zixuan Zhou Sybil Derrible Yangqiu Song Zhongming Lu Department of Computer Science and Engineering Hong Kong University of Science and TechnologyClear Bater BayKowloonHong Kong Special Administrative Region Division of Environment and Sustainability Hong Kong University of Science and TechnologyClear Water BayKowloonHong Kong Special Administrative Region Department of Civil Materials and Environmental EngineeringUniversity of Illinois at ChicagoChicagoIllinoisUnited States

Room air conditioners (RACs) are crucial household appliances that consume substantial amounts of electricity. Their efficiency tends to deteriorate over time, resulting in unnecessary energy wastage. Smart meters have become popular to monitor electricity use of home appliances, offering underexplored opportunities to evaluate RAC operational efficiency. Traditional supervised data-driven analysis methods necessitate a large sample size of RACs and their efficiency, which can be challenging to acquire. Additionally, the prevalence of zero values when RACs are off can skew training. To overcome these challenges, we assembled a dataset comprising a limited number of window-type RACs with measured operational efficiencies from 2021. We devised an intuitive zero filter and resampling protocol to process smart meter data and increase training samples. A deep learning-based encoder–decoder model was developed to evaluate RAC efficiency. Our findings suggest that our protocol and model accurately classify and regress RAC operational efficiency. We verified the usefulness of our approach by evaluating the RACs replaced in 2022 using 2022 smart meter data. Our case study demonstrates that repairing or replacing an inefficient RAC can save electricity by up to 17 %. Overall, our study offers a potential energy conservation solution by leveraging smart meters for regularly assessing RAC operational efficiency and facilitating smart preventive maintenance.

关键词： Room air conditioning Operational energy efficiency Smart meter Deep learning Smart preventive maintenance

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Throughput-Maximized Network Deployment in a Multi-NIB-Aided ISAC Network

Throughput-Maximized Network Deployment in a Multi-NIB-Aided...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Park, Ki-Hong Alouini, Mohamed-Slim Chen, Yunfei King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia Durham University Department of Engineering Durham United Kingdom

ISBN: (纸本)9798350370218

In this paper, we consider the deployment problem of integrated sensing and communication (ISAC) network in sparsely populated area where the distributions of broadband users and Internet-of-things (IoT) devices are disjoint and therefore traditional network infrastructure can be under-utilized. Network-in-a-box which is lightweight, portable, and flexible to support different type of networks is the emerging technology for either one-off network or under-served network environments. We formulate the total network throughput maximization problem to optimally deploy multiple NIBs enabling an ISAC network supporting both conventional broadband data and sensed IoT data modeled by short packet communication (SPC) framework. Under the delay and minimum throughput requirements of sensing data for IoT devices, we will propose the suboptimal iterative solution to find location of NIBs, association between NIB and user, and coding rate in SPC framework and verify the feasibility by simulation. © 2023 IEEE.

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Mitigating Urban Heat Island Effects: A Review of Innovative Pavement Technologies and Integrated Solutions

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Structural Durability & Health Monitoring 2024年第5期18卷 525-551页

作者： S.F.Ismael A.H.Alias N.A.Haron B.B.Zaidan Abdulrahman M.Abdulghani Department of Civil Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Central Region Heavy Civil DivisionGranite ConstructionWoodlandsTexas77381USA SP Jain School of Global Management LidcombeSydneyNSW2141Australia Department of Communication Technology and Network Faculty of Computer Science and Information TechnologyUniversiti Putra MalaysiaSerdang43400Malaysia

In this review paper,we present a thorough investigation into the role of pavement technologies in advancing urban *** analysis traverses the historical evolution of these technologies,meticulously evaluating their socio-economic and environmental impacts,with a particular emphasis on their role in mitigating the urban heat island *** evaluation of pavement types and variables inﬂuencing pavement performance to be used in the multi-criteria decision-making(MCDM)framework to choose the optimal pavement application are at the heart of our *** serves to assess a spectrum of pavement options,revealing insights into the most effective and sustainable *** highlighting both the existing challenges and potential innovative solutions within theﬁeld,this paper aims to offer a directional compass for future urban planning and infrastructural *** review not only synthesizes the current state of knowledge but also aims to chart a course for future exploration,emphasizing the critical need for innovative and environmentally sensitive pavement tech-nologies in the creation of resilient and sustainable urban environments.

关键词： Pavement sustainability urban heat island environmental impact of pavements innovative pavement solutions economic and social implications sustainable development in urban areas

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Monkeypox Detection with Deep-Learning and Ensemble of Features: A Study 4

Monkeypox Detection with Deep-Learning and Ensemble of Featu...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Taniar, David Damaševičius, Robertas Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Monash University Faculty of Information Technology MelbourneVIC3800 Australia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas44249 Lithuania

ISBN: (纸本)9798350387490

There is a growing global prevalence of infectious diseases in the human population and it is imperative to have precise diagnosis and therapy in order to effectively cure these diseases. Monkeypox (Mpox) is an infectious disease that can cause varying degrees of skin infection, ranging from minor to severe. The recent spread of Mpox has been confirmed as Public Health Emergency of International Concern (PHEIC) due to its large infection rate. This study aims to develop a deep-learning (DL) scheme for Mpox identification. To enhance the detection accuracy, this study considered preprocessed skin images for the analysis and employed the binary classification with Ensemble of Deep-Features (EDF) using 5-fold cross validation. The outcome of this investigation validate that the suggested deep learning tool enables the attainment of a detection accuracy of 100% on the augmented images from the Monkeypox Skin Images Dataset (MSID). Compared to the existing results in the literature, this research provides a significant result on the chosen MSID database. © 2024 IEEE.

关键词： Classification Ensemble of deep-features Healthcare Illness Monkeypox

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