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15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information Technology, applications and Tools, FITAT 2019

作者： Liang, Xiao-Cong Wu, Tsu-Yang Lee, Yu-Qi Chen, Chien-Ming Yeh, Jyh-Haw Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Computer Science Boise State University Idaho United States

ISBN: (纸本)9789811397134

Authentication and key agreement schemes in smart grid have received much attentions by researchers in recent years. Recently, Mahmood et al. proposed a secure pairing-based anonymous key exchange scheme in smart grid. However, we demonstrate that their scheme is insecure against an ephemeral secret leakage attack and an impersonation attack according to the Canetti-Krawczyk adversarial model in this paper. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Smart power grids

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Design and Assessment of Representative Hybrid Clinical Trials using Health Recommender System 6

Design and Assessment of Representative Hybrid Clinical Tria...

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6th International Workshop on Health Recommender Systems, HealthRecSys 2024

作者： Neehal, Nafis Anand, Vibha Bennett, Kristin P. Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Biomedical AI-Healthcare and Life Sciences IBM T.J. Watson Research Center CambridgeMA02141 United States Institute of Data Exploration and Applications Mathematical Sciences and Computer Science Departments Rensselaer Polytechnic Institute TroyNY12180 United States

Incorporating real-world data (RWD) into clinical trials can enhance trial efficiency, diversity, and generalizability. This paper introduces the Framework for Research in Synthetic Control Arms (FRESCA), which uses a novel Recommender System combined with Equity Adjustment strategies to design and evaluate Representative Hybrid Clinical Trials (HCTs). FRESCA employs a novel matching algorithm through its recommendation system to select suitable patients from RWD while ensuring that the selected population is representative of the target demographic. This dual approach improves both patient selection and trial outcomes by balancing statistical appropriateness and equity. Simulations based on data from two existing randomized clinical trials (RCTs) show that using FRESCA to recommend patients from RWD and apply equity adjustments enhances internal validity and generalizability. Our analysis indicates that combining matching and equity adjustments yields more accurate treatment effect estimates and fair population representation, even with reduced RCT control group sizes. In contrast, using either method alone may result in biased outcomes. The flexibility of FRESCA to simulate various HCT scenarios makes it a valuable tool for advancing equitable and efficient clinical trial designs. © 2024 Copyright for this paper by its authors.

关键词： Clinical research

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A Reliable Approach for Automatic Grading of Diabetic Retinopathy from Fundus Images using Feature Extraction and Fusion

A Reliable Approach for Automatic Grading of Diabetic Retino...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： R.Anita Jasmine M. Ramla M. Ramesh Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai India Department of Computer Applications SRM Institute of Science and Technology Chennai India Department of Computer Science SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

Diabetic Retinopathy (DR) is widely recognized as the primary cause of visual impairment worldwide. Early intervention is crucial in preventing irreversible vision loss. Ophthalmologists conventionally utilize fundus cameras to capture retinal images and detect indications of diabetic retinopathy, thereby facilitating prompt diagnosis. The progress made in computational intelligence has significantly enhanced our ability to identify, predict, and categorize DR. The focus of this research is to present a proficient framework for the DR grading. The framework proposed employs Laplacian filters to reduce undesirable image distortions and enhance the overall quality of the images. The methodology involves the utilization of transfer learning through the pre-existing VGG19 Convolutional Neural Network architecture to pull out features from diverse pooling layers. The amalgamation of extracted features from multiple layers results in the formation of feature vectors. These vectors are utilized as inputs for training various machine learning algorithms, with the objective of identifying the most effective model for evaluating the severity of DR. Significantly, the research provides a rationale for its forecasts, guaranteeing the transparency of the algorithm and comprehensibility of the model, thereby validating its reliability. The model's exceptional precision renders it a promising candidate for broad implementation in clinical identification and prediction of DR, potentially augmenting global patient outcomes.

关键词： Diabetic retinopathy Analytical models Machine learning algorithms Image analysis Visual impairment Predictive models Feature extraction Prediction algorithms Vectors Reliability

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Online Affinity of Instructing Methods for Using Personal-Efficacy and Reading Capacious During Covid-19 5th

Online Affinity of Instructing Methods for Using Personal-Ef...

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5th International Conference on Communication, Device and Networking, ICCDN 2021

作者： PremaLatha, V. Sreedevi, E. Sivakumar, S. Nayak, Soumya Ranjan Bhoi, Akash Kumar Ghosh, Uttam Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L University Guntur Vaddeswaram522502 India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Kattankulathur603203 India Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India KIET Group of Institutions Delhi-NCR Ghaziabad201206 India Directorate of Research Sikkim Manipal University Sikkim Gangtok737102 India Department of Computer Science and Data Science Meharry School of Computational Sciences Nashville NashvilleTN United States

ISBN: (纸本)9789811920035

Now a days in EFL procedure of education the ability of reading became as significant belief and personal-efficacy reading as a basic understanding for students. By monitoring the acknowledged participates under the ballpark figure of large studying and methods of understanding, the impact of their observation is premeditated on reading of each one’s personal-efficacy. On a daily routine all these things are comparatively considered which are put into effect by teachers of handful in number. Approach towards exhibiting Extensive reading (ER) is inspected to be "more expensive, difficult, and time-consuming". Method of recognition of elements in a various way for effective impact in putting its efforts to utilize for its empowerment. Paper has been segregated into two contexts: Association with attitude is considered as primary one and attitude is considered as secondary one. Whether knowledge work is understood by student or not is considered as the impact of ER by the first review. Procedure which are convenient is taken as the observations of student and is analysed as second one. The examinations are quantifiable to utilize the observations as information in terms of subjective way taken from students who belong to first academic year of reading course in a systematic way and 603 details of undergraduate students from KLEF of Guntur were chosen as participants for extant examination. In ER programme of includes and excludes "comprehension reading work" is treated as fundamental in the proposal of disclosures. In case of any, "the programme appeared to positively affect contributing students". Techniques of classification like "decision tree and Mixed Model database Miner (MMDBM)" are employed in this paper which leads to improvements of post-test to pre-test in ER group. Observations of students in ER results as optimistic and algorithm of MMDBM which leads to accuracy in higher rate in pre-test and post-test detection. © 2023, The Author(s), under exclusive lice

关键词： Students

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Etiseg-Net: Edge-Aware Self Attention to Enhance Tissue Segmentation in Histopathological Images

SSRN

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SSRN 2023年

作者： Rashmi, R. Girisha, S. Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Digital pathology employing Whole Slide Images (WSIs) plays a pivotal role in cancer detection. Nevertheless, the manual examination of WSIs for the identification of various tissue regions presents formidable challenges due to its labor-intensive nature and subjective interpretation. Convolutional Neural Network (CNN) based semantic segmentation algorithms have emerged as valuable tools for assisting in this task by automating ROI delineation. The incorporation of attention modules and carefully designed loss functions has shown promise in further augmenting the performance of these algorithms. However, there exists a notable gap in research regarding the utilization of attention modules specifically for tissue segmentation, thereby constraining our comprehension and application of these modules in this *** study introduces ETiSeg-Net (Edge-aware self attention to enhance Tissue Segmentation), a CNN-based semantic segmentation model that uses a novel edge-based attention module to achieve effective delineation of class boundaries. In addition, an innovative iterative training strategy is devised to efficiently optimize the model parameters. The study also conducts a comprehensive investigation into the impact of attention modules and loss functions on the efficacy of semantic segmentation models. Qualitative and quantitative evaluations of these semantic segmentation models are conducted using publicly available datasets. The findings underscore the potential of attention modules in enhancing the accuracy and effectiveness of tissue semantic segmentation. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Tensor MIMO Perfect-Equalizer Design Using New Projection-Based Method and Approximation-Error Analysis

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IEEE Systems Journal 2024年第1期18卷 668-679页

作者： Chang, Shih-Yu Wu, Hsiao-Chun Kao, Yi-Chih San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for Ai Applications Chungli32003 Taiwan National Yang Ming Chiao Tung University Network and System Division Information Technology Service Center Hsinchu30010 Taiwan

As real environments are quite complex, multiple-input-multiple-output (MIMO) channels should be characterized by the multidimensional-multirelational models. Tensor linear algebra has been emerging as a powerful and promising tool for such MIMO channel modeling and estimation. However, to the best of our knowledge, there hardly exists any algebraic framework of MIMO channel equalization when the channel is modeled as an arbitrary-dimensional transfer tensor (a tensor whose entries are all rational z-functions). In this work, we first propose a new method based on the projection, namely, 'projection-based method,' to determine whether a given transfer tensor is a full-rank tensor or not and carry out the inversion of an invertible transfer tensor. Our idea is employing the projection subspace of the system transfer tensor to simplify the pertinent inversion process. Second, we design the finite-impulse-response (FIR) realization to approximate the inverse of an arbitrary transfer tensor such that all entries of the approximated inverse transfer tensor are finite-degree polynomials of z. Third, we also analyze the computational and memory complexities required by our proposed new projection-based method for checking the full-rank condition of a given transfer tensor and for undertaking the tensor inversion. Our developed new theories and analysis in this work can be applied to determine the FIR model-orders of an approximated inverse MIMO transfer tensor often encountered in signal processing, control, communications, etc. © 2007-2012 IEEE.

关键词： FIR filters

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Patterns of Widespread Structural Abnormalities in Autism Spectrum Disorder using Diffusion Tensor Imaging

Patterns of Widespread Structural Abnormalities in Autism Sp...

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Communication, computer sciences and Engineering (IC3SE), International Conference on

作者： Dilip Kumar Bharti Sanjay Kumar Singh Mukesh Kumar Vishal Kumar Kanaujia Manisha Aeri Computer Science and Engineering-Data Science ABES Engineering College Ghaziabad India Department of Computer Science & Applications Kashi Institute of Technology Varanasi U.P India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand Data Science Department Computer Science and Engineering ABES Engineering College Ghaziabad INIDA

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Diffusion tensor imaging (DTI) is a neuroimaging approach that lets in for the visualization and quantification of the structural integrity of white depend fibers in the brain. In latest years, DTI has come to be an essential tool for investigating the underlying neurobiological mechanisms of autism spectrum sickness (ASD), a neurodevelopmental disorder characterized via problems in social communication and repetitive behaviors. Through the usage of DTI, researchers have identified good sized styles of structural abnormalities in the brains of individuals with ASD. these abnormalities encompass changes in the microstructure of white remember fibers, along with decreased fiber density, changes in fiber directionality, and decreased connectivity between mind areas. Additionally, there may be evidence of bizarre mind development in early formative years, with a few research finding that these structural variations are present even in very younger children with ASD. One constant finding in DTI studies of ASD is reduced white rely integrity inside the corpus callosum, a large fiber package deal that connects the two hemispheres of the mind. This abnormality has been connected to deficits in social communiqué and language in people with ASD, as well as difficulties in integrating records from distinctive brain areas. The results showed extensive structural abnormalities in people with ASD in comparison to TD individuals.

关键词： Neuroimaging Optical fiber sensors Autism Visualization Variable speed drives Optical fiber networks Motors

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Physiological Signals based Student Grades Prediction using Machine Learning

Physiological Signals based Student Grades Prediction using ...

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Information Technology (OCIT), OITS International Conference on

作者： Vanshikha Agarwal Naeem Ahmad Md Gulzarul Hasan Department of Data Science PSPH Manipal Academy of Higher Education Manipal India Department of Computer Applications National Institute of Technology Raipur India

Amidst today’s fast-paced world, the escalating levels of stress among students have become a significant concern, and it appears to be inversely related to their academic performance. There have been several types of research on the link between stress and grade prediction. Our study aims to leverage the available dataset about the stress level in such a way that can predict students’ grades to comment on the student’s performance. The dataset is collected using the wearable device during three exams containing electrodermal activity, skin temperature, and heart rate from 7680 student subjects. Our approach involves employing a variety of machine learning and deep learning algorithms, including Decision Trees Regressor, CNN Regressor, KNN Regressor, Random Forest Regressor, and Support Vector Regressor models. Through our investigation, we aim to demonstrate the potential of wearable devices in extracting meaningful insights from physiological signals to predict students’ academic outcomes. The challenges associated with wearable observations present unique opportunities for addressing and mitigating stress-related issues among students, and this study harnesses the power of a publicly available dataset to contribute to this important area of research.

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An efficient Quasi-Affine Transformation Evolutionary algorithm with fixed dimension updating and its application in UAV 3D path planning

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9755-9781页

作者： Sung, Tien-Wen Zhao, Baohua Zhang, Xin Lee, Chao-Yang Fang, Qingjun Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou China Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a kind of swarm-based collaborative optimization algorithm that solves the problem of a position deviation in a DE search by using the co-evolution matrix M instead of the cross-control parameter CR in the differential evolution algorithm (DE). However, QUATRE shares some of the same weaknesses as DE, such as premature convergence and search stagnation. Inspired by the artificial bee colony algorithm (ABC), we propose a new QUATRE algorithm to improve these problems that ranks all the individuals and evolves only the poorer half of the population. In an evolving population, individuals of different levels intersect with dimensions of different sizes to improve search efficiency and accuracy. In addition, we establish a better selection framework for the parent generation individuals and select more excellent parent individuals to complete the evolution for the individuals trapped in search stagnation. To verify the performance of the new QUATRE algorithm, we divide the comparison algorithm into three groups, including ABC variant group, DE variant group, and QUATRE variant group, and the CEC2014 test suite is used for the comparison. The experimental results show the new QUATRE algorithm performance is competitive. We also successfully apply the new QUATRE algorithm on the 3D path planning of UAV, and compared with the other famous algorithm performance it is still outstanding, which verifies the algorithm's practicability. © 2024 - IOS Press. All rights reserved.

关键词： Optimization

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An Efficient data Storage Technique for User Files in Cloud

An Efficient Data Storage Technique for User Files in Cloud

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Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CCHESS), International Conference on

作者： R. Eswari A. Vamshi M. Sumaiya Sultan Department of Computer Applications National Institute of Technology Tiruchirappalli India Department of Data Science St.Joseph’s College Tiruchirappalli India

A remote data storage service provided by cloud providers is known as cloud storage. It substitutes an online-accessible central storage repository for the need for nearby physical storage devices. Cloud storage is efficient due to its wide range of accessibility. An efficient approach for managing file storage in cloud computing environments is presented in this paper. The proposed method utilizes the concept of dividing files into blocks and combining them to generate file hashes using Merkle hash tree (MHT). These file hashes facilitate quick and reliable data integrity verification. Upon transmission of multiple files to the Cloud Service Provider (CSP), a secure storage process is implemented within the cloud environment. The data packets are organized and managed through a $B+$ tree structure. The proposed scheme also supports dynamic file operations. Where users can modify files, and for each modification, a new file hash is generated by merging the block hashes through MHT. By combining the efficiency of Merkle hash tree and B+ tree structures, this proposed system offers a dependable solution for effectively managing files in cloud storage. This method makes a significant contribution to the fields of cloud computing and file management by improving data security, integrity, and accessibility.

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