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Detection of Diabetic Retinopathy from Retinal Images Using DenseNet Models

computer Systems science & engineering 2023年第4期45卷 279-292页

作者： R.Nandakumar P.Saranya Vijayakumar Ponnusamy Subhashree Hazra Antara Gupta Department of ECE K S R Institute for Engineering and TechnologyTiruchengode637215India Department of Computer Science and Engineering SRM Institute of Science and TechnologyChennai603203India Department of Electronics and Communication and Engineering SRM Institute of Science and TechnologyChennai603203India

A prevalent diabetic complication is Diabetic Retinopathy(DR),which can damage the retina’s veins,leading to a severe loss of *** treated in the early stage,it can help to prevent vision *** since its diagnosis takes time and there is a shortage of ophthalmologists,patients suffer vision loss even before ***,early detection of DR is the necessity of the *** primary purpose of the work is to apply the data fusion/feature fusion technique,which combines more than one relevant feature to predict diabetic retinopathy at an early stage with greater *** procedures for diabetic retinopathy analysis are fundamental in taking care of these *** profound learning for parallel characterization has accomplished high approval exactness’s,multi-stage order results are less noteworthy,especially during beginning phase *** Connected Convolutional Networks are suggested to detect of Diabetic Retinopathy on retinal *** presented model is trained on a Diabetic Retinopathy Dataset having 3,662 images given by *** results suggest that the training accuracy of 93.51%0.98 precision,0.98 recall and 0.98 F1-score has been achieved through the best one out of the three models in the proposed *** same model is tested on 550 images of the Kaggle 2015 dataset where the proposed model was able to detect No DR images with 96%accuracy,Mild DR images with 90%accuracy,Moderate DR images with 89%accuracy,Severe DR images with 87%accuracy and Proliferative DR images with 93%accuracy.

关键词： Convolutional Neural Networks vision loss pathogenic blood vessels DenseNet AlexNet ResNet

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A Comparative Analysis of Child Activity Recognition Using Machine Learning 5th

A Comparative Analysis of Child Activity Recognition Using M...

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5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Kumar, Narendra Arora, Shefali Aggrawal, Lakshay Computer Science and Engineering National Institute of Technology Punjab Jalandhar144008 India Computer Science and Engineering DAV Institute of Engineering and Technology Punjab Jalandhar144008 India

ISBN: (纸本)9789819761050

This research study aims to create a smart child activity recognition device combines a Raspberry Pi-pico, sensors for Internet of things (IoT). The system includes several sensors for monitoring human motion, pulse, and location parameters. Different types of machine learning and deep learning algorithm is used for analyzing data to identify various kinds of activities in children including sitting, standing, running, and climbing the stairs. This study utilizes a Raspberry Pi-pico, which is a cost-effective as well as tiny single-board computer central processing unit for an infant monitoring system. It can be easily integrated with various sensors and devices, which make it a suitable foundation for developing an IoT-based tracking system. It is able to link to the Internet through Wi-Fi, allowing for surveillance remotely. The dataset collected from 22 different children based on their features like sitting, standing, running, and climbing the stairs. The model was trained using a data of child mobility and behavior, distinguishing activities such as sitting, standing, running, and stair climbing. There are six different classifiers: LSTM, SVM, ANN, KNN, AdaBoost, and Logistic Regression. When applied to pre-processed data, SVM outperformed other classification methods. The classification accuracy reached 98.81%. The experimental results indicate that the proposed system may identify behavior and it will keep parents remotely. The technology continuously monitors and analyzes the child’s environment and behavior to ensure their safety and well-being. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector machines

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Nitrogen-based redox couple regulated anionic redox to long-term cycling stability of Li and Mn-rich layered oxide cathode for Li-ion batteries

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材料科学技术（英文版） 2025年第12期215卷 157-166页

作者： Zhijun Wu Chenchen Li Panyu Gao Xin Zhang Yue Lin Xuebin Yu Yongfeng Liu Wenping Sun Yinzhu Jiang Mingxia Gao Hongge Pan Yaxiong Yang Institute of Science and Technology for New Energy Xi'an Technological UniversityXi'an 710021China Department of Materials Science Fudan UniversityShanghai 200433China State Key Laboratory of Silicon and Advanced Semiconductor Materials and School of Materials Science and Engineering Zhejiang UniversityHangzhou 310027China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of ChinaHefei 230026China Institute of Science and Technology for New Energy Xi'an Technological UniversityXi'an 710021China State Key Laboratory of Silicon and Advanced Semiconductor Materials and School of Materials Science and Engineering Zhejiang UniversityHangzhou 310027China

Lithium and manganese-rich layered oxides(LMROs)have attracted extensive attention and are promis-ing cathode materials for next-generation lithium ion batteries due to their high capacities and high energy ***,LMRO cathode suffers from severe capacity and voltage fading originating from irreversible surface oxygen ***,we propose a facile redox couple strategy by intro-ducing nitroxyl radicals species to regulate the surface anionic redox reaction of LMRO ***-tial electrochemical mass spectroscopy,X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy analyses demonstrate that during charge process,the peroxide ion O22-on the surface gen-erated from the oxidation of lattice O2-could be reduced back to stable O2-by redox couple in time,thus avoiding oxygen evolution and structure degradation,as well as enhancing bulk oxygen redox *** enhanced LMRO electrode delivers a high capacity of 220.3 mAh g-1 at 1 *** excellent cycling stability with a capacity retention of 94.4％is achieved after 500 cycles,as well as a suppressed voltage decay with only 1.12 mV per cycle.

关键词： Nitroxyl radicals Redox couple 2 6 6-tetramethylpiperidinooxy Cycling stability Li and Mn-rich layered oxides

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Hybrid deep learning based digital image watermarking using GAN-LSTM and adaptive gannet optimization techniques

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Multimedia Tools and Applications 2024年 1-31页

作者： Shedole, Sambhaji Marutirao Santhi, V. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Katpadi Vellore632014 India

Nowadays, multimedia technology is progressing everyday. It is very easy to duplicate, distribute and modify digital images with online editing software. Image security and privacy are critical aspects of the multimedia revolution. Therefore, digital image watermarking offers an alternative way out for image authentication. Currently, watermarking methods are crucial for safeguarding digital images. Several traditional watermarking approaches have been developed to protect images using spatial domains and transformations. Watermarking techniques that are more traditional are less resistant to repeated attacks. Deep learning-based watermarking has recently gained traction, greatly improving the safety of visual images in a variety of common applications. This study presents a robust and secure digital watermarking method for multimedia content protection and authentication. The watermark image is first transformed using the hybrid wavelet transform, and then it is encrypted using a chaos encryption algorithm. The cover image is simultaneously subjected to neighborhood-based feature extraction. Leveraging these extracted features, a novel Adaptive Gannet Optimization algorithm (AGOA) is employed to determine the optimal embedding location. Subsequently, the watermarked image is seamlessly integrated and extracted using the hybrid Generative adversarial network-based long short-term memory (GAN-LSTM) approach within the identified optimal region. Decryption and Inverse transformation are then used to get the original watermark image. Several previous methods, such as DNN, Deep-ANN, and Deep-CNN, are used to evaluate the performance of the proposed method. This technique improves multimedia content protection and authentication by guaranteeing strong and secure watermarking. The proposed method for digital image watermarking produced a peak signal-to-noise ratio of 46.412 and a mean square error of 24.512. Therefore, the proposed method performs well in digital image wa

关键词： Wavelet transforms

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A Novel Music Recommendation System Using Filtering Techniques

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Informatica (Slovenia) 2024年第4期48卷 595-610页

作者： Vashishtha, Srishti Varshney, Deepika Sarin, Eva Kaur, Simran Computer Science and Engineering Dept. Bharati Vidyapeeth’s College of Engineering GGSIPU New Delhi India Computer Science and Engineering Dept. Jaypee Institute of Information Technology Noida India

With the growth of the World Wide Web, a large amount of music data is available on the Internet. A large number of people listen to music online rather than downloading and listening offline. But only some sites provide personalized and accurately recommended songs while they listen to an auto-playing playlist. Hence the need for recommendation systems arises. Two approaches can be applied to the recommendation system: content based filtering and collaborative filtering. While in content-based filtering approach, analysis on the songs’ content which has been preferred by the user in history is done and the songs with relative similarity are recommended. While latter suggests songs that certain users of similar listening pattern have preferred. But collaborative filtering-based recommendation systems not only requires time to attain stability but also might recommend unsuitable music because it is not personalized to each user’s preference. Also, the latter requires the songs to be listened by a few users already to recommend it any other user. Hence for overall analysis, answers to few questions need to be implemented in recommendation systems: the very first one is how the properties should be analyzed, next one is how the analysis should be done and last is how the songs related to the user’s preference should be chosen. So, for suggesting the better system which solves these three questions as well as provides better and personalized recommendations, In this paper we build a collaborative filtering as well as content based filtering recommendation system, where various factors determine the essence of the songs, i.e. liveliness, keys used, loudness, pitch, valence, etc., are analyzed for comparison. From the experimental analysis in has been identified that content-based filtering technique performed best on KNN machine learning classifier with accuracy of 85%. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Collaborative filtering

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Large Scopus Data Sets and Its Analysis for Decision Making

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Annals of Data science 2024年第2期11卷 589-618页

作者： Singh, Prem Kumar Department of Computer Science and Engineering Gandhi Institute of Technology and Management-Vishakhapatnam Andhra Pradesh530045 India

Recently several authors paid attention towards Scopus Data analysis for intellectual measurement of institutes or authors. It is well known that the SCOPUS contains more than 34,346 peer reviewed Journals from different subjects with 3 lakh conferences. It is difficult to measure the performance or expertise of any institute or author in the given domain for admission, job, and ranking or other decision making process. The reason is several manipulations started in document and citation count via strategic authors or institute which can be measured via average author publications, number of funding, collaborations and retracted papers. It is happening due to rogue editor or business strategic of educationalist for profit. However these types of misconduct impacts lot to real researcher which forces brain drain. To resolve this issue, the current paper provides a way to measure the intellectual achievement of an institute or author based on several metrics. The proposed method is illustrated using the SCOPUS data sets and it’s metric for critical understanding. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Decision making

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Comparing HAProxy Scheduling Algorithms during the DDoS Attacks

IEEE Networking Letters

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IEEE Networking Letters 2024年第2期6卷 139-142页

作者： Kumar, Anmol Somani, Gaurav Agarwal, Mayank Indian Institute of Technology Patna Department of Computer Science and Engineering Patna800013 India Central University of Rajasthan Department of Computer Science and Engineering Ajmer305817 India

Web-services have become most common IT enablers today. Cyber attacks such as the distributed denial of service (DDoS) attacks pose availability concerns which may result into service outages and consequently financial and reputation losses. Load balancing software such as HAProxy (high availability proxy) is an important building block of the Web service delivery today. Load balancing software provides incoming request distribution among Web-servers and admission control which may even help with combating DDoS attacks. In this letter, we study and explore the performance of different load distribution or scheduling strategies in the presence of DDoS attacks. With the help of attack experiments, we find that First, HDR, Source, and URI scheduling algorithms performs best for both static and Poisson arrivals. © 2019 IEEE.

关键词： Scheduling algorithms

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A Novel Approach for Lightweight Sign Language Recognition Leveraging Google Mediapipe and Deep Neural Net 1

A Novel Approach for Lightweight Sign Language Recognition L...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Siju, Ijas Selvam, Prabu Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350352931

The advent of technologies like Deep Learning has revolutionized human interaction, transcending language and disability barriers. Sign Language Recognition (SLR) systems have emerged as vital tools, facilitating seamless communication between individuals and between humans and machines through Interfaces. However, existing SLR systems pose significant challenges, including the demand for high accuracy, low latency, simplicity in development and deployment, lightweight design for edge compatibility, and efficient resource utilization during training. Addressing these challenges necessitates developing a system that balances training data accuracy, ease of training, and resource efficiency. Conventional Deep learning approaches often rely on high-dimensional image datasets for training HGR models, resulting in trade-offs between latency, accuracy, complexity, and resource requirements. To mitigate these challenges, this paper harnesses the capabilities of Google Mediapipe to extract a 21-point hand landmark vector from each training image. By leveraging this low-dimensional data, we train model that are accurate, lightweight, less complex, and exhibit low latency, making them compatible with edge devices. The hand gesture images were collected for several gestures, such as peace, okay, and stop. These images will be converted to landmark vectors using Mediapipe. These landmark points were used to train the Deep Neural Network architecture developed in Tensorflow. The model was trained with each hand gesture assigned with a common word in English and the proposed model was tested with a live webcam stream. The proposed model is developed in Python in the Google Colab IDE with the help of libraries and frameworks like OpenCV, Tensorflow and Google Mediapipe. The proposed model outperforms current state-of-the-art approaches by achieving an accuracy of 9 4. 8 8 %. © 2024 IEEE.

关键词： Convolutional neural networks

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Chi-Square Target Encoding for Categorical Data Representation: A Real-World Sensor Data Case Study

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SN computer science 2025年第3期6卷 1-22页

作者： Anitha, M. Savarimuthu, Nickolas Bhanu, S. Mary Saira Department of Computer Applications National Institute of Technology Trichy India Department of Computer Science and Engineering National Institute of Technology Trichy India

Feature engineering is critical for improving machine learning performance (ML), especially when handling categorical data. Traditional encoding methods, such as one-hot and label encoding, often result in challenges like high dimensionality and loss of category significance. To address these limitations, this study introduces a novel Chi-Square Target Encoding (CSTE) approach, which transforms categorical variables into numerical representations by leveraging the chi-square statistic to evaluate the association between categories and the target variable, preserving information effectively. Unlike conventional techniques, CSTE uses Chi-square test values to generate target-based encoded representations, ensuring reliable transformation without information loss. Comprehensive empirical evaluations were conducted on various datasets for binary classification, showcasing CSTE’s superiority over methods like regularized target encoding, basic target encoding, and fuzzification. A case study on real-world sensor data further validated its efficiency and scalability for large-scale data-driven applications. The proposed CSTE method achieved an average increment rate of 3.9447%, surpassing one-hot and fuzzification (1.0430%) and regularized target encoding (1.8308%). Classification outcomes demonstrated F1-scores exceeding 0.90 and AUC values nearing 0.99 across diverse datasets, highlighting its robustness. Furthermore, the reduced dimensionality significantly enhanced inference time while maintaining high accuracy. The CSTE method offers a robust framework for categorical data transformation, addressing limitations of traditional encoding techniques. It improves the interpretability and efficiency of categorical data representation, boosting ML performance. This innovative approach is well-suited for applications across various domains involving categorical data. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Categorical data encoding Chi- Square statistics Data distribution preservation Feature engineering Machine learning Target encoding

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Micro-expressions: a survey

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Multimedia Tools and Applications 2024年第18期83卷 53165-53200页

作者： Jain, Ankita Bhakta, Dhananjoy Department of Computer Science and Engineering Indian Institute of Information Technology Ranchi Jharkhand Ranchi834010 India

This paper surveys some of the most important recent works related to micro-expression analysis. It includes discussions on algorithms for spotting and recognizing micro-expressions, their performances, databases, and new feature descriptor frameworks. Here, we use a reverse chronological order to discuss the state-of-the-art in a work-specific fashion. We provide a questionnaire-based comparative study of the literature and discuss future directions of micro-expression analysis. We expect this survey to expose micro-expressions to those interested in pursuing research in this emerging field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Machine learning

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