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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Shanufa Nazrin, P.S. Benifa, J.V. Bibal Anu, K.P. Vijayakumar, Aiswarya Dept. of Computer Science Kerala Thrissur India Indian Institute of Information Technology Dept.of Computer Science and Engineering Kerala kottayam India

ISBN: (纸本)9798350394580

HAR is specified as the crucial task within the field of computer vision and artificial intelligence. Main aim of HAR is to observing and labelling different human activity images of distinct data sources by machines such as computer. HAR has applications in various fields. CNN model is an appropriate model for HAR task, due to their ability to automaticaly acquire relevant spatial features, capture hierarchical representations, and handle input of various sizes and types. So it is utilized for developing HAR model. This model can detect objects and also can classify images. The model contains wide ranging catogories. ShuffleNetV2 is a particular architecture within it, mainly for efficient and lightweight deep learning task. This paper develops an intelligent HAR using ShuffleNetV2 model which acquired an accuracy about 69.52 percentage. © 2023 IEEE.

关键词： HAR Human activity ShuffleNetV2

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Collaborative, Privacy-Preserving Federated Learning Framework for the Detection of Diabetic Eye Diseases

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SN computer science 2024年第8期5卷 1100页

作者： Gulati, Seema Guleria, Kalpna Goyal, Nitin Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendergarh India

Diabetes Mellitus is one of the chronic metabolic disorders that is easily identified by elevation in blood sugar levels. The high blood sugar levels over time can lead to substantial damage to the blood vessels, heart, eyes, kidneys, and nerves. Diabetic Eye Diseases (DED) such as Diabetic Macular Edema (DME), Diabetic Retinopathy (DR), and glaucoma, are serious complications of diabetes that can result in irreversible vision loss. Thus, affecting the health and quality of life of the population worldwide. The primary objective of the proposed work is to evaluate the efficacy of a privacy-preserved Federated Learning (FL) framework for detecting DEDs. To detect DEDs a collaborative and privacy-preserving Federated Deep Learning (FDL) framework using a lightweight MobileNetV2 architecture is proposed. The aggregation scheme selected for the FDL framework is FedProx and to achieve a high accuracy score the proximal term, µ is tuned with values ranging from µ= (0.01, 0.02, 0.03, 0.04 and 0.05). The impact of the variance in the proximal term on model performance is analyzed, and the results show that higher µ (0.03,0.4 and 0.5) values provide more stable and consistently high precision, recall, and F1-scores. The findings suggest that FedProx aggregation successfully stabilizes the performance of the framework with the least standard deviation of 0.035 in accuracy at µ = 0.05. The balance between accuracy and stability is a key factor in the successful application of federated learning in disease prediction. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence Collaborative training Diabetic eye diseases Federated learning Privacy-protection

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EvauleBlock: Evaluating Answers in E-Learning Applications with Enhanced Security and Intelligence through DRL and Blockchain Integration 3

EvauleBlock: Evaluating Answers in E-Learning Applications w...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Parthasarathy, R. Kiruthiga Devi, M. Karthick, V. Kumar, P. Mahendran, Rakesh Kumar Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Dr. Mgr Educational and Research Institute Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

Evaluating the candidate answers during online examination by a human examiner is wearisome task. Along with that, security concerns also associated in the E-Learning environment in terms of false data injection and modification attacks respectively. The state of the art work, either focus on designing intelligent and robust question answering model using Machine Learning (ML)/Deep Learning (DL) algorithm, or designing a secure framework for E-Learning environment using blockchain. To neutralize that issue, we design a secure and intelligent model for E-Learning environment using Deep Reinforcement Learning (DRL) and blockchain methodology respectively named EvauleBlock. The EvauleBlock composed of four major processes such as data assortment, data pre-processing, intelligent answer evaluation, and blockchain storage. In data assortment stage, the training of the proposed DRL algorithm named Multi Agent Deep Deterministic Policy Gradient (MADDPG) is taken place by crawling the related question answering data from the various websites. Followed by, we have performed pre-processing for the candidate written answers using adequate pre-processing steps. Once the answers are pre-processed, it is then provided to the MADDPG algorithm which employs four agents for validating the candidate answers by checking the semantic, syntactic, keyword, and context similarity scoring respectively. At last, the overall scores are securely stored in the blockchain using Proof of Work (PoW) consensus algorithm. The implementation of this work is carried out using python 3.11.0 tool and performance of the proposed model are validated and compared with adequate performance metrics. The results are promising when compared to the conventional models. © 2024 IEEE.

关键词： Answer Evaluation Blockchain Deep Reinforcement Learning E-Learning Question Answering system

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OUT-OF-DOMAIN UNLABELED DATA IMPROVES GENERALIZATION 12

OUT-OF-DOMAIN UNLABELED DATA IMPROVES GENERALIZATION

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12th International Conference on Learning Representations, ICLR 2024

作者： Saberi, Amir Hossein Movasaghinia, Mohammad Hosein Najafi, Amir Motahari, Seyed Abolfazl Heidari, Alireza Khalaj, Babak H. Department of Electrical Engineering Department of Computer Engineering Sharif Center for Information Systems and Data Science Iran Sharif Institute for Convergence Science & Technology Sharif University of Technology Tehran Iran

We propose a novel framework for incorporating unlabeled data into semi-supervised classification problems, where scenarios involving the minimization of either i) adversarially robust or ii) non-robust loss functions have been considered. Notably, we allow the unlabeled samples to deviate slightly (in total variation sense) from the in-domain distribution. The core idea behind our framework is to combine Distributionally Robust Optimization (DRO) with self-supervised training. As a result, we also leverage efficient polynomial-time algorithms for the training stage. From a theoretical standpoint, we apply our framework on the classification problem of a mixture of two Gaussians in Rd, where in addition to the m independent and labeled samples from the true distribution, a set of n (usually with n m) out of domain and unlabeled samples are given as well. Using only the labeled data, it is known that the generalization error can be bounded by ∝ (d/m)1/2. However, using our method on both isotropic and non-isotropic Gaussian mixture models, one can derive a new set of analytically explicit and non-asymptotic bounds which show substantial improvement on the generalization error compared to ERM. Our results underscore two significant insights: 1) out-of-domain samples, even when unlabeled, can be harnessed to narrow the generalization gap, provided that the true data distribution adheres to a form of the "cluster assumption", and 2) the semi-supervised learning paradigm can be regarded as a special case of our framework when there are no distributional shifts. We validate our claims through experiments conducted on a variety of synthetic and real-world datasets. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Polynomial approximation

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Cloud Removal with Multisource Data Fusion of Remote Sensing Images for Agricultural Applications 9

Cloud Removal with Multisource Data Fusion of Remote Sensing...

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9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Kaur, Ravneet Maini, Raman Tiwari, Reet Kamal Punjabi University Department of Computer Science Engg. Patiala India AIT Chandigarh University Department of Computer Science Engg. India Indian Institute of Technology Department of Civil Engineering Ropar India

ISBN: (纸本)9798350319651

The cloud is one of the major problems that impacts remote sensing images especially when the imagery is acquired through an optical satellite sensor. This is because the satellite image is generally acquired during the daytime and clouds reflect sunlight. Many studies have been conducted to remove the problem of the cloud from satellite imagery with different strategies but it remains the challenge to overcome the cloud from the remote sensing data. In this article, an approach has been followed to overcome the impact of the remote sensing data with the help of fusion and machine learning techniques. In this case, two satellite imagery were acquired from different sensors i.e., ISRO's SCATSAT-1 and NASA's MODIS. SCATSAT-1 is advantageous to overcome the impact of the clouds whereas MODIS provides multispectral information in various bands. Both the data are merged to generate the cloud high-resolution multispectral dataset. Moreover, the fused output is classified as analyzing its performance with respect to the MODIS classified dataset. The results show that fused output achieved better accuracy as compared to the MODIS- classified dataset. With the effective removal of clouds, more information can be observed from the earth which can be utilized in a variety of applications such as agriculture, cryosphere and natural hazard study. © 2023 IEEE.

关键词： Image fusion

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Performance Analysis of Health Care Expense Anticipation using Computational Intelligence 1

Performance Analysis of Health Care Expense Anticipation usi...

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Gopalakrishnan, A. Kumarganesh, S. Divya Bharathi, G. Anthoniraj, S. Natarajan, Suresh Kumar Gochhait, Saikat Knowledge Institute of Technology Department of Artificial Intelligence and Data Science Salem India Knowledge Institute of Technology Department of Electronics and Communication Engineering Salem India Mahendra Institute of Technology Department of Computer Science and Engineering Namakkal India School of Computer Science and Engineering Ramanagara India Symbiosis International Deemed University Symbiosis Institute of Digital and Telecom Management Maharashtra Pune India

ISBN: (纸本)9798350348637

The escalating costs of healthcare globally necessitate the development of accurate prediction models to address the financial strain on individuals, families, businesses, and governments. This research employs linear regression models to forecast healthcare expenditures, analyzing the intricate relationship between various factors and costs. A comprehensive literature survey explores topics such as the impact of environmental pollution on healthcare spending, prediction of surgical expenses, budget impact analysis for cancer treatment, and the integration of big data in healthcare infrastructure investments. The methodology involves gathering a diverse dataset encompassing patient demographics, medical history, treatment methods, and associated expenditures. Exploratory data analysis employs Pearson's correlation coefficient to identify trends and correlations between variables. Two linear regression models are proposed and compared, with the second model excluding non-significant variables for enhanced accuracy and interpretability. Results indicate that the second model, with refined feature selection, outperforms the first in terms of mean squared error and R-squared values. A comparative analysis of different studies underscores the diverse findings in healthcare economics, ranging from the impact of CO2 emissions on health to predicting surgery costs and budgetary savings in cancer treatment. The research contributes to the growing body of knowledge in healthcare economics, providing decision-makers with a valuable tool for precise expenditure estimation. The emphasis on linear regression models aids stakeholders in understanding factors influencing healthcare costs, guiding resource allocation, policy creation, and financial planning for more sustainable and effective healthcare systems. © 2024 IEEE.

关键词： Budget control

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Efficient Data Transmission using PSO Algorithm in Wireless Sensor Networks 15

Efficient Data Transmission using PSO Algorithm in Wireless ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kalaiselvi, K. Thilak, K.Deepa Devi, K.Lalitha Kumaresan, K. Malathi, M. Faculty of Engineering and technology Department of Networking and communications SRM Institute of science and technology Kattankulathur Tamilnadu Chennai India Department of Computer science and Engineering Sathyabama Institute of Science and Technology India Department of CSE KSR College of Engineering Tiruchengode Tamilnadu Namakkal India Tamilnadu Namakkal India

ISBN: (纸本)9798331300579

A Wireless Sensor Network is made up of a large number of small, low-power sensor nodes that are randomly or manually distributed across an uncontrolled target ***, the WSNs' main constraint is the sensor nodes' restricted power *** a result, energy saving at sensor nodes is the most difficult barrier for WSNs' long-term *** and low-energy routing are two well-known optimization tasks that have received a lot of attention in the quest to extend the life of wireless sensor networks (WSNs).In a periodic data gathering application, the Particle Swarm Optimization (PSO) technique is employed to overcome this *** monitor the environment on a regular basis and send data to the base station (BS), which analyses the data and draws conclusions about the field activities.A cluster is used to send the data to the base station.A cluster head receives data from sensor nodes and transmits it to the cluster head, which combines the information and sends it to the base *** packet delivery ratios and throughput have both been *** a result, total energy consumption is reduced. © Grenze Scientific Society, 2024.

关键词： Particle swarm optimization (PSO)

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Distortion Minimization with Age of Information and Cost Constraints 21

Distortion Minimization with Age of Information and Cost Con...

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21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Jayanth, S. Pappas, Nikolaos Bhat, Rajshekhar V. Department of Electrical Engineering Indian Institute of Technology Dharwad Dharwad India Department of Computer and Information Science Linköping University Linköping Sweden

ISBN: (纸本)9783903176553

We consider a source node deployed in a real-time monitoring application that needs to sample a stochastic process and convey its state timely and accurately to a destination over a wireless ON/OFF channel. The source can either process a raw sample to determine its current state and transmit that information or transmit the raw sample and let the destination determine the state. The source is subjected to an average cost constraint, and it cannot sample, process, and transmit at all the time instants due to the associated costs. When the destination does not receive information, it uses the previous information as an estimate of the current state, which, if it matches the actual state at the source, the distortion is considered to be zero. The objective is to minimize average expected distortion subject to constraints on the average expected age of information (AoI) of states of interest and costs incurred by the source, where the AoI of a state increases if no status update is received, else drops to unity. We derive a stationary randomized policy (SRP) to solve the formulated problem, for which we obtain the expression for the expected AoI under the SRP using a lumpability argument on the two-dimensional discrete-time Markov chain formed using AoI and instantaneous distortion as states. We extensively study the impact of the system parameters on the average distortion under the SRP and draw significant conclusions. © 2023 IFIP.

关键词： Stochastic systems

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2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023

作者： Ghosh, Swarnadipto Saha, Chinmoy Siddiqui, Jawad Y. Indian Institute of Space Science and Technology Department of Avionics Thiruvananthapuram India Royal Military College of Canada Department of Electrical and Computer Engineering Kingston Canada

ISBN: (纸本)9798350328264

In this article, a detailed characterization of Tensor Surface Impedance Matrix (TSIM) for orthogonally discrete inductive and capacitive meta unit-cell has been performed. Four orthogonally distributed digitally coded (i.e 0 0, 0 1, 1 0, 11) states have been assigned for both orthogonally discretized inductive and capacitive meta unit-cell. Tensor Surface Impedance Matrix (TSIM) is implemented for each unit cell to characterize the isotropic, non-homogeneous nature of Zsurf. From dispersion relation, the variation of phase velocity has been characterized for both Inductive Meta unit-cell (IMU) as well as Capacitive Meta unit-cell (CMU). Further, Cross Correlation between similar coded IMU and CMU has been performed to extract the linear dependency of those complementary structures. Also, the Cross correlation between the proposed orthogonally discrete unit-cells have been performed to extract the equivalent impedance coupling factor (Zc). Effective Zsurf vs direction of surface wave propagation (θk) is characterized for each orthogonally discrete unit cell for both proposed IMU and CMU. © 2023 IEEE.

关键词： Capacitive meta unit-cell Inductive meta unitcell Non-Homogeneous unit-cell Sinusoidally modulated LWA Tensor Surface Impedance

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A Haar-Cascading and Deep Learning Driven Enhanced Mechanism on Cloud Platform for Real-Time Facial Emotion Detection 5

A Haar-Cascading and Deep Learning Driven Enhanced Mechanism...

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5th International Conference on Energy, Power, and Environment, ICEPE 2023

作者： Kumar, Anil Navdeep Kumar, Adarsh Swain, Smruti Rekha Gupta, Ishu Singh, Ashutosh Kumar National Institute of Technology Department of Computer Applications Kurukshetra India International Institute of Information Technology Department of Computer Science Bangalore India Indian Institute of Information Technology Madhya Pradesh Bhopal India

ISBN: (纸本)9798350313123

Real-time emotion detection utilizes computer vision that aims to recognize and categorize emotions. However, it contains challenges, including the intricate nature of emotions and the variations in their expression across individuals. The accuracy of the system can also be affected by environmental factors such as background noise, lighting, and other people. Having access to a diverse and comprehensive dataset is essential for training algorithms that can accurately detect emotions in real time. In order to tackle these challenges, we utilized CNN and seven emotions to identify their corresponding facial expressions. By implementing appropriate activation functions like ELU, ReLU, and Leaky ReLU, and training with the dataset Facial Expression Recognition 2013 (FER-2013) that contains over 30,000+ images of facial expressions and emojis, as we increase the epoch the accuracy of this model increases, we achieved an impressive accuracy of 94.74% by using ReLU on 100 enochs. © 2023 IEEE.

关键词： Emotion Recognition

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