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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： N, Srinivasa Gupta Valarmathi, B. Kavitha, A. Gopini, Sai Bhuvan Seela, Hari Kumar Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced of Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9783031770777

Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, high rates of maternal as well as infant morbidity and mortalities are recorded. This research utilizes Artificial Intelligence (AI) with machine learning algorithms to forecast and address maternal health hazards right at their onset stage. The current research utilizes the concept of AI along with many Machine Learning (ML) methods like the Ensemble Learning Model (ELM), Random Forest (RF), K-Nearest Neighbour (KNN), Decision-Tree (DT), XG-Boost (XGB), Cat Boost (CB), and Gradient Boosting (GB), along with Synthetic Minority Over-sampling Technique (SMOTE) algorithm used for dealing with the problem class imbalance within the data set. SMOTE algorithm is utilized for the dataset balancing process. The handling system involves refining data preprocessing with the help of feature engineering and robust data cleaning which makes sure that anomalies do not erode the reliability of the predictive model. The existing methods [1] used RF (90%), DT (87%), XGB (85%), CB (86%), and GB (81%) algorithms and were compared with the accuracies of the proposed models like Logistic Regression (LR), Ensemble Learning Bagging (ELB), Ensemble Learning Stacking (ELS), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The existing methods used only imbalance dataset. The accuracies of the proposed models with using SMOTE algorithm (balanced dataset) are LR (61.33%), KNN (81%), ELB (92.33%), ELS (90.66%) CNN (40.67%), RNN (59.67%), LSTM (54%), GRU (56%) respectively. Among these methods, ELB achieved 92.33% of accuracy with using SMOTE algorithm using imbalanced dataset. Whereas the accuracies of the proposed models without using SMOTE algorithm (imbalanced dataset) are LR (66.09%), KNN (68.47%)

关键词： Convolutional neural networks

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AI-Based Child Care Parental Control System 4

AI-Based Child Care Parental Control System

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Jayasekara, Udara Maniyangama, Hansindu Vithana, Kalhan Weerasinghe, Tharana Wijekoon, Janaka Panchendrarajan, Rrubaa Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka Department of Computer System Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398090

Due to the prevalence of the COVID-19 epidemic around the globe, children were compelled to engage in remote learning through online platforms, hence mobile phone has become one of their predominant devices. Mobile device with Internet access offers a major outlet for education, entertainment, and social connection, but this combination can lead to several significant bad sequences such as online exploitation, harmful addictions, and other negative impacts of online social networking. To address harmful effects, parental controls are becoming more crucial, yet Sri Lankan parents are less aware of this. Consequently, this study proposes a parental control system to monitor their child's activities. Android, Microsoft Azure, Java, Python, OpenCV, MySQL, and FastAPI are among the most prominent technologies utilized in the proposed application's development. The suggested approach focuses primarily on the Sri Lankan context and aims to enhance parental digital literacy while safeguarding children from cyber threats. Yielded results showed the proposed mobile application for the identification of toxic words, drugs & alcohol content, game character images, and Instagram Sinhala comments severity as 94%, 95%, 97%, and 55% respectively in controlled experiments. © 2022 IEEE.

关键词： Control systems

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Street Light Cum Garbage System Optimal Design Based on IOT 2

Street Light Cum Garbage System Optimal Design Based on IOT

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2nd International Conference on Emerging Trends in information Technology and engineering, ic-ETITE 2024

作者： Rajesh, G. Deepa, J. Ezilarasan, M.R. Syed Fiaz, A. Siva Rama Lingham, N. Jagannathan, J. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350328202

Street lights currently use more energy than other types of lighting because of an inefficient mechanism that makes the bulbs use a lot of electricity. The suggested approach uses various sensors on intelligent streetlights to keep an eye on and manage the lights. It has sensors for measuring temperature, brightness, and power that control dimming levels and keep track of status. One Zigbee network serves as the connection point for these lights. In the modification, a cloud- and IoT-based installation was made. Through Zigbee -based data transmission, sensor values are recorded on a distant server known as the Cloud. Additionally, it lowers electricity theft. We include a trash can notice in addition to this street light idea. When the garbage can is whole, a machine automatically notifies the business as people approach the sun, and the brightness of the street lights changes or is increased. © 2024 IEEE.

关键词： Light emitting diodes

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LKAW: A Robust Watermarking Method Based on Large Kernel Convolution and Adaptive Weight Assignment

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computers, Materials & Continua 2023年第4期75卷 1-17页

作者： Xiaorui Zhang Rui Jiang Wei Sun Aiguo Song Xindong Wei Ruohan Meng Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Instrument Science and Engineering Southeast UniversityNanjing211189China School of Teacher Education Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Science Engineering Nanyang Technological UniversitySingapore

Robust watermarking requires finding invariant features under multiple attacks to ensure correct *** learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread *** existing methods use 3×3 small kernel convolution to extract image features and embed the ***,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the *** address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss *** uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel ***,the modification of the embedded watermarking on the cover image is extended to more *** the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight ***,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image *** experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise.

关键词： Robust watermarking large kernel convolution adaptive loss weights high-frequency wavelet loss deep learning

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Fast Second-order Method for Neural Networks under Small Treewidth Setting

Fast Second-order Method for Neural Networks under Small Tre...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Li, Xiaoyu Long, Jiangxuan Song, Zhao Zhou, Tianyi Stevens Institute of Technology Department of Computer Science HobokenNJ United States South China University of Technology School of Software Engineering Guangzhou China University of California Berkeley The Simons Institute for the Theory of Computing BerkeleyCA United States University of Southern California Department of Computer Science Los AngelesCA United States

ISBN: (纸本)9798350362480

Training neural networks is a fundamental problem in theoretical machine learning. Second-order methods are rarely used in practice due to their high computational cost, even they converge much faster than first-order methods. The state-of-the-art result for the second-order method to train an over-parameterized neural network can run in O(log(1/ϵ)) iterations and each iteration has O(mnd + n3) running time, where n represent the number of data points, d is the dimension of the feature space of each data and m is the width of the neural network [Brand, Peng, Song and Weinstein ITCS 2021]. In this work, we further improve the convergence rate and the running time for each iteration to O(loglog(1/ϵ)) and O(mdτ2) respectively, where m = Ω(n4) and τ is the treewidth of the data matrix and usually very small. Our algorithm has a quadratic convergence rate, so it can be regarded as a truly second-order algorithm. To the best of our knowledge, our algorithm is the first to achieve a quadratic convergence rate for neural network training, with a per-iteration running time of O(mdτ2). © 2024 IEEE.

关键词： Trees (mathematics)

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PLAME: Piecewise-Linear Approximate Measure for Additive Kernel SVM (Extended abstract) 40

PLAME: Piecewise-Linear Approximate Measure for Additive Ker...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Chan, Tsz Nam Li, Zhe Leong Hou, U. Cheng, Reynold Shenzhen University Department of Computer Science and Software Engineering China Alibaba Cloud University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China The University of Hong Kong Department of Computer Science and Musketeers Foundation Institute of Data Science Hong Kong

ISBN: (纸本)9798350317152

Additive Kernel SVM has been extensively used in many applications, including human activity detection and pedestrian detection. Since training an additive kernel SVM model is very time-consuming, which is not scalable to largescale datasets, many efficient solutions have been developed in the past few years. However, most of the existing methods normally fail to achieve one of these three important conditions which are (1) low classification error, (2) low memory space, and (3) low training time. In order to simultaneously fulfill these three conditions, we develop the new piecewise-linear approximate measure (PLAME) for training additive kernel SVM models. Experimental results verify that this approach can achieve the best trade-off between accuracy, memory space, and training time compared with different types of state-of-the-art methods. © 2024 IEEE.

关键词： Additives

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PROBEXPERT: An Enhanced Q&A Platform for Reducing Time Spent on Learning and Finding Answers 7

PROBEXPERT: An Enhanced Q&A Platform for Reducing Time Spent...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Thennakoon, Kamal Ekanayake, Dasun Marapana, Thanura Ranasinghe, Ashen Wijendra, Dinuka R. Gamage, Anjalie Sri Lanka Institute of Information Technology Department of Computer Science & Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665421683

The World Wide Web contains a wide range of material from a variety of fields. However, when concerns towards the computer science domain, information users find on the internet may not be up-to-date due to the rapid pace of change and having to spend less time on the internet for researching and debugging tasks is an added luxury. Having an expertise level while providing answers through a platform is convenient for users, yet when a user signs into a platform, the user must start from the beginning, regardless of the level of competence in the field. Moreover, not having a proper way to evaluate the existing programming knowledge is another obstacle. To address mentioned complications, researchers of this paper have introduced a new e-learning platform- 'ProbExpert'. The platform has been constructed with machine learning and deep learning approaches such as NLP, keyword extraction, semantic information analysis, cosine similarity, and information summarization. With aforesaid technologies, ProbExpert provides systems in automated answering, optimized answer generation, structured question-based quiz evaluation together with a fully automated portfolio generation with a novel user ranking algorithm based on the bell curve. © 2022 IEEE.

关键词： Automation

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E-tutor: Comprehensive Student Productivity Management System for Education 4

E-tutor: Comprehensive Student Productivity Management Syste...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Silva, Kumuditha Induwara, Rashmin Wimukthi, Malshan Poornika, Sathsarani Samaratunge Arachchillage, Udara Srimath S. Jayalath, Thilini Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

With the advancement of technology, e-learning has emerged as predominant in the education sector. As students, parents, and educators acknowledged, adopting e-learning can offer several benefits over traditional learning techniques. Since more individuals are becoming acclimated to online learning platforms, these online platforms can provide a simple, instructive, and efficient mode of delivery. This novel approach could be improved with the aid of Artificial Intelligence (AI) to comprehend consumers more thoroughly and provide valuable and better-suited services. Most sectors in education, including universities, swiftly adapted to new educational methodologies because of their flexibility and productivity. Nevertheless, there are some downsides that young demography experiences, such as less instructiveness, distraction due to the absence of teachers, and poor IT literacy. Consequently, these drawbacks would recede the capability of students to assimilate content during the lecture. Therefore, the main objective of this research is to implement an E-learning platform with AI learning analytics to enhance students' performance regularly while reducing the significant drawbacks of the E-learning platforms. This research consists of students' focus detection, essay-based answer evaluation, note summarization, mind map generation, and personalized guidance facilities. © 2022 IEEE.

关键词： Object detection

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Guardian - Smart Assistant Tool for Visually Impaired People 4

Guardian - Smart Assistant Tool for Visually Impaired People

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Amarasinghe, C.K. Pinto, R.D.S.P. Sudusinghe, K.N. Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

At present, with the advancement of technology, various devices and solutions have been found to aid the visually impaired community (VI Community). Even with the countless technological breakthroughs, yet they face many problems performing the most basic functions in daily life. Identifying the objects, they use daily, identifying a person, whether it's someone they know or not, and their emotions, and reading a text information displayed anywhere, without the assistance from another person are the basic issues we deal with and try to resolve using a tool consisting of a pair of spectacles with an inbuilt camera that is integrated with a mobile application. The inbuilt camera will capture the image of a text containing label, an object, or a person which will be then detected, analyzed, and recognized and will be converted to Speech using Google TTS engine and produced through the headphones giving the output to the user. Tesseract OCR, YOLO algorithm, and TensorFlow models have been used for each feature of the tool respectively. This tool will be very beneficial to a blind person as it mitigates them the frustration caused by being incapable of performing daily activities without any assistance from another person. © 2022 IEEE.

关键词： Face recognition

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SPAVIS: Mobile Application for Visually Impaired Based on Assistive software and Volunteerism 4

SPAVIS: Mobile Application for Visually Impaired Based on As...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Mahir, M.A.M. Hussain, M.N.M. Perera, R.D.D. Upendra, Y.A.M. Wickramarathne, C.J. Kasthurirathna, Dharshana Department of Computer Science & Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

Sri Lankan population accounts up to almost one million visually impaired individuals out of which are mostly students and young individuals. As the educational structure for the visually impaired improves with funds, blind schools, and free education, assistance with minute needs for most visually impaired individuals comes at a cost. There are many assistive technologies, such as audio books, screen magnifiers, braille books, and screen readers, prevalent around the island. However, there are several limitations to these technologies, mainly their availability and affordability. In Sri Lanka, many individuals, societies, clubs, and many more are willing to volunteer to help those in need, even those that require physical attention. As much as it is anticipated to aid those in need, there is very little attention to the ways it can be done. Hence, this research provides a way to develop a user-friendly mobile application with assistive software and volunteerism to aid visually impaired students with their daily needs. © 2022 IEEE.

关键词： Students

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