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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Ahmad, Javed Ahmad, Shahnawaj Arif, Mohd. Nazim, Mohd Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida201110 India Bennett University Department of Computer Science and Engineering Uttar Pradesh Greater Noida201310 India School of Computing Science and Engineering Department of Computer Applications Uttar Pradesh Greater Noida203201 India Noida Institute of Engineering and Technology Department of CSE Uttar Pradesh Greater Noida201306 India

ISBN: (纸本)9798350306118

Computing may be a profoundly examined subject within the technological and financial world, and numerous huge players in the software industry are working for the improvement of computing services. With the help of a literature review, this work has identified that the classification of cloud computing (CC) does not specify how to classify CC based on industry-driven professional articles, as most of this study has been based on academic distributions. This may limit the ability of the research study to provide a consolidated view of the most current advances in this topic. CC has recently captured the attention of scholars, financial professionals, and specialists. Many of these experts have proposed a variety of applications based on various cloud-based techniques, frameworks, and essentials, which have led to different interpretations, specifications, deployment models, and service models. Therefore, a complete characterization and classification of CC are needed to address the above issue. The ultimate purpose of this research is to classify CC in the literature, including the set of security concerns, history, concepts, technologies, and challenges. © 2023 IEEE.

关键词： Cloud security

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Automated Diagnosis of Breast Cancer using Combined Features and Random Forest Classifier 6

Automated Diagnosis of Breast Cancer using Combined Features...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Muduli, Debendra Priyadarshini, Rojalina Barik, Ram Chandra Nanda, Surendra Kumar Barik, Rabindra K. Roy, Diptendu Sinha C.V.Raman Global University Bidya Nagar Mahura Janla Department of Computer Science and Engineering Bhubaneswar752054 India Kalinga Institute of Industrial Technology School of Computer Applications Bhubaneswar India National Institute of Technology Department of Computer Science and Engineering Meghalaya India

ISBN: (纸本)9798350346961

In recent years, several computer-aided diagnosis (CAD) models have been proposed for the early detection of breast cancer to minimize the mortality rate among women. However, the CAD model needs better accuracy with fewer features to improvise the model's efficiency. In this work, we propose an effective automated CAD model for mammogram image classification using the least number of features. The model presented here employs a two-dimensional discrete wavelet transform for feature extraction from mammogram images. The combined technique called PCA and LDA utilize feature reduction and select significant features. The combined reduced feature matrices are then fed into a random forest classifier, which is used to make the classification between normal and abnormal mammography images. Conventional datasets including INbreast, MIAS, and DDSM were used to test and evaluate the proposed CAD model with minimal features.. © 2023 IEEE.

关键词： Principal component analysis

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WoEEE: a hybrid approach for enhancement of categorical data transformation

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International Journal of Data science and Analytics 2025年 1-29页

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

Efficient encoding of categorical data is essential for enhancing the performance of machine learning (ML) models, statistical methods, particularly when handling high-cardinality features and complex relationships between variables. Traditional encoding methods have notable limitations. One-hot encoding (OHE) often results in high-dimensional feature spaces, reducing model efficiency and predictive accuracy; label encoding (LE) can introduce misleading ordinal relationships; binary encoding (BE) may create excessively large feature representations for high-cardinality categories; weight of evidence (WoE) is prone to overfitting when rare categories are present; and entity embedding (EE) is less interpretable, and may perform poorly on unseen categories. To address these challenges, this paper introduces weight of evidence–entity embedding encoding (WoEEE), a novel encoding technique that integrates the strengths of both WoE and EE. WoE, a statistical approach, encodes categorical variables based on their influence on the target variable, while EE, a DL-based technique, transforms categories into continuous embeddings to capture complex, nonlinear relationships. The proposed WoEEE encoding method first applies WoE to identify and encode significant categories, followed by EE to generate low-dimensional embeddings. Comprehensive empirical evaluations were conducted on various binary and multiclass classification datasets to assess the effectiveness of the proposed WoEEE encoding method. The results, obtained using classifiers such as random forest (RF), Decision Tree (DT), logistic regression (LR), eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), demonstrate that WoEEE improves performance over the conventional encoding techniques as well as advanced methods including fuzzification-based encoding, deep-learned embedding, deep feature embedding, and regularized target encoding. On the Adult dataset, WoEEE achieves an F1-score of 0.9

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Visualizing the Full Spectrum Optimization of K-Nearest Neighbors From Data Preprocessing to Hyperparameter Tuning and K-Fold Validation for Cardiovascular Disease Prediction

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第2期49卷 355-374页

作者： Joseph, Jeena Kartheeban, K. Department of Computer Applications Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Computer Applications Marian College Kerala Kuttikkanam India Computer Science and Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India

Cardiovascular disease (CVD) is a prominent cause of death worldwide. This alarming need requires an accurate prediction model using machine learning that can detect and help prevent or mitigate the risk. This study focuses on this issue and has come up with new dimensional capabilities to enhance the K-Nearest Neighbors (KNN) algorithm to predict cardiovascular diseases at an early stage by incorporating various techniques for data preprocessing and feature selection thereby improving the efficiency of the model. The proposed model identifies the most relevant features using Principal Component Analysis. The main innovation revolves around fine tuning the hyperparameter of K-Nearest Neighbors, specifically the choice of neighbors (K), using a data driven approach to ensure accuracy across different datasets. The performance of the optimized K-Nearest Neighbors algorithm is evaluated using the Framingham heart disease dataset. This model achieved an impressive prediction accuracy of 92.46% and outperformed methods that solely rely on traditional K-Nearest Neighbors. As machine learning techniques plays an important role in the development of prediction models for early detection and prevention of cardiovascular disease, this model can be considered as a valuable tool for healthcare professionals and researchers. The core contribution of this study lies in offering a comprehensive optimization of the traditional K-Nearest Neighbors (KNN) algorithm. This includes robust data preprocessing using the Hampel filter for outlier removal, feature selection through Principal Component Analysis (PCA), and performance enhancement using grid search for hyperparameter tuning combined with 10-fold cross-validation. Unlike prior studies that apply KNN with minimal adjustments, this research emphasizes the importance of an end-to-end machine learning pipeline. This holistic refinement significantly improves the predictive performance and reliability of KNN for cardiovascular diseas

关键词： Nearest neighbor search

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Handwriting Recognition Using Wasserstein Metric in Adversarial Learning

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SN computer science 2023年第1期4卷 1-10页

作者： Jangpangi, Monica Kumar, Sudhanshu Bhardwaj, Diwakar Kim, Byung-Gyu Roy, Partha Pratim Department of Computer Science and Engineering IIT Roorkee Roorkee 247667 India Department of Computer Engineering and Applications GLA University Mathura Mathura 281 406 India Department of IT Engineering Sookmyung Women’s University Seoul 04310 South Korea CCSIT Teerthanker Mahaveer University Moradabad 244001 India

Deep intelligence provides a great way to deal with understanding the complex handwriting of the user. Handwriting is challenging due to its irregular shapes, which vary from one user to another. In recent advancements in artificial intelligence, deep learning has unprecedented potential to recognize the user’s handwritten characters or words more accurately than traditional algorithms. It works well on the concept of the neural network, many algorithms such as convolutional neural network (CNN), recurrent neural network (RNN), and long short term memory (LSTM) are the best approaches to get high accuracy in handwritten recognition. However, much of the existing literature work lacks the feature space in a scalable manner. A model consisting of CNN, RNN, and transcription layer called CRNN and the Adversarial Feature Deformation Module (AFDM) is used for the affine transformation to overcome the limitation of existing literature. Finally, we propose an adversarial architecture comprised of two separate networks;one is seven layers of CNN with spatial transformation networks (STN), which act as a generator network. Another is Bi-LSTM, with the transcription layer working as a discriminator network and applying Wasserstein’s function to verify the model effectiveness using IAM word and IndBAN dataset. The performance of the proposed model was evaluated through different baseline models. Finally, It reduced the overall word error and character error rate using our proposed approach. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AFDM CRNN IAM and IndBAN datasets STN WGAN

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Enhancing Smart Home Automation with Arduino and Artificial Intelligence 13

Enhancing Smart Home Automation with Arduino and Artificial ...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Ahmad, Nazia Hussain, Sajithunisa Khan, Rubina Liyakat Quraishi, Suhail Javed Singh, Anupam George, Remya P. Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Mriirs Computer Applications Haryana Faridabad India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350380583

The current instigation aims to examine how AI and IoT work hand in hand to use smart home automation with a special focus on the application based on Arduino microcontrollers. The suggested system focuses on increasing comfort, convenience, and safety of the home spaces by applying AI algorithms. The system comprises a number of integrated sensors and actuators that gather real time data and the data is analyzed and decisions are made by an AI control module. The strategies of design considerations, implementation, and the assessment of the energy usage, comfort level for the user and the security aspects of the system are also provided in the study. Exploitation of the proposed experimental benchmark confirms potential advancement compared to traditional systems, including the viability of successful, comprehensive smart home platforms. Keywords: Smart Home Automation, Energy Efficiency, Internet of Things (10 T), Artificial Intelligence (AI), Arduino Uno © 2024 IEEE.

关键词： Smart homes

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Secure Digital Image Watermarking Technique Based on ResNet-50 Architecture

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Intelligent Automation & Soft Computing 2024年第6期39卷 1073-1100页

作者： Satya Narayan Das Mrutyunjaya Panda Department of Computer Science and Engineering GIET UniversityGunupurOdisha765022India Department of Computer Science and Applications Utkal UniversityBhubaneswarOdisha751004India

In today’s world of massive data and interconnected networks,it’s crucial to burgeon a secure and efficient digital watermarking method to protect the copyrights of digital *** research primarily focuses on deep learning-based approaches to improve the quality of watermarked images,but they have some *** overcome this,the deep learning digital image watermarking model with highly secure algorithms is proposed to secure the digital ***,quantum logistic maps,which combine the concept of quantum computing with traditional techniques,have been considered a niche and promising area of research that has attracted researchers’attention to further research in digital *** research uses the chaotic behaviour of the quantum logistic map with Rivest–Shamir–Adleman(RSA)and Secure Hash(SHA-3)algorithms for a robust watermark embedding process,where a watermark is embedded into the host *** way,the quantum chaos method not only helps limit the chance of tampering with the image content through reverse engineering but also assists in maintaining a high level of imperceptibility and strong robustness with efficient extraction or detection of watermark *** Wavelet Transformation(LWT)is a potential and computationally efficient version of traditional Discrete Wavelet Transform(DWT)where the host image is divided into four sub-bands to offer a multi-resolution view of an image with greater flexibility in watermarking ***,considering the robustness against attacks,a pre-trained Residual Neural Network(ResNet-50),a convolutional neural network with 50 layers deep,is used to better learn the complex features and efficiently extract the watermark from the *** integrating RSA and SHA-3 algorithms,the proposed model demonstrates improved imperceptibility,robustness,and accuracy in watermark extraction compared to traditional *** achieves a Peak Signal-to-Noise Ratio(PSNR)of 49.83%,a Structural Simi

关键词： Image watermarking quantum logistics Rivest-Shamir-Adleman(RSA) Secure Hash(SHA-3) Lifting Wavelet Transformation(LWT) ResNet-50 deep learning secure communication

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Predicting opinion evolution based on information diffusion in social networks using a hybrid fuzzy based approach

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International Journal of Information Technology (Singapore) 2023年第1期15卷 87-100页

作者： Uthirapathy, Samson Ebenezar Sandanam, Domnic Department of Computer Applications National Institute of Technology Tamil Nadu Tiruchirappalli 620015 India Department of Computing Science and Engineering Vignan’s Foundation for Science Technology & Research Vadlamudi Andra Pradesh Guntur 522213 India

Social media plays an important role in disseminating information and analysing public and government opinions. The vast majority of previous research has examined information diffusion and opinion analysis separately. This study proposes a new framework for analysing both information diffusion and opinion evolution. The change in opinion over time is known as opinion evolution. To propose a new model for predicting information diffusion and opinion analysis in social media, a forest fire algorithm, cuckoo search, and fuzzy c-means clustering are used. The forest fire algorithm is used to determine the diffuser and non-diffuser of information in social networks, and fuzzy c-means clustering with the cuckoo search optimization algorithm is proposed to cluster Twitter content into various opinion categories and to determine opinion change. On different Twitter data sets, the proposed model outperformed the existing methods in terms of precision, recall, and accuracy. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer applications and Management.

关键词： Cuckoo search Forest fire algorithm Fuzzy C-means clustering Information diffusion Opinion analysis Social network

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An IOT based Framework for Project Management 13

An IOT based Framework for Project Management

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13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022

作者： Kumar, Devendra Tewari, Pragya ABES Engineering College Department of Computer Applications Uttar Pradesh Ghaziabad India School of Computing Science & Engineering Dept of Computer Science & Engineering Galgotias University India

ISBN: (纸本)9781713857938

The Internet of Things is transforming the world due to which organizations are facing many difficulties. The transformation due to these technologies is affecting several areas and project management is not an exception. The Internet of Things can help project managers to manage their projects as well as their types of equipment in an effective way. The connection between IoT devices and project management can allow businesses to achieve new innovations and services in a more effective manner while reducing the risk factor. In this paper, we proposed a framework for effective project management based on the Internet of Things (IoT). © Grenze Scientific Society, 2022.

关键词： Project management

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Automated Detection of Language Disorders in Children Using NLP and Machine Learning

Automated Detection of Language Disorders in Children Using ...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Malathi, P. Legapriyadharshini, N. Nair, Shruthi S Sujatha, M.P. Sadaieswaran, R. Thirumalaikumari, T. Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Application Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Agni College of Technology Department of Computer Science and Engineering Chennai India Tagore College of Arts and Science Department of Computer Applications Chennai India Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India

ISBN: (纸本)9798350309140

The purpose of the study is to address the essential need for a more efficient and objective technique for detecting language disorders in children, overcoming the constraints of standard manual evaluation systems. Existing systems, which utilize subjective evaluations and manual scoring, frequently result in lengthy processes and possible errors. To solve these issues, the proposed system automates the identification process by combining Natural Language Processing (NLP) and Machine Learning (ML). The system achieves a breakthrough in accuracy by normalizing text and extracting a comprehensive collection of linguistic features using advanced NLP techniques. The implementation involves difficult data preprocessing, extensive feature extraction, and the application of a cutting-edge Support Vector Machine (SVM) classifier. The proposed system achieves a 15% increase in accuracy, a 12% increase in precision, a 10% increase in recall, and an 11% increase in F1 score over existing systems. The confusion matrices show improved prediction skills, while accuracy across varied linguistic samples demonstrates the system's adaptability. Finally, the proposed system not only speeds up the detection of language disorders but also provides the groundwork for deeper insights into linguistic development, with revolutionary potential in educational and therapeutic settings. © 2024 IEEE.

关键词： Adversarial machine learning

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