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Metaheuristic Based Clustering with Deep Learning Model for Big data Classification

computer Systems science & engineering 2023年第1期44卷 391-406页

作者： R.Krishnaswamy Kamalraj Subramaniam V.Nandini K.Vijayalakshmi Seifedine Kadry Yunyoung Nam Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India Department of Biomedical Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatore641021India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600077India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two *** the same time,clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in ***-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial *** data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce *** this motivation,this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics(MR-HDBCC).The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big *** addition,the MR-HDBCC technique involves three distinct processes namely pre-processing,clustering,and *** proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)techni-que which is capable of detecting random shapes and diverse clusters with noisy *** improving the performance of the DBSCAN technique,a hybrid model using cockroach swarm optimization(CSO)algorithm is developed for the exploration of the search space and determine the optimal parameters for density based ***,bidirectional gated recurrent neural network(BGRNN)is employed for the classification of big *** experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.

关键词： Big data data classification clustering mapreduce dbscan algorithm

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Security to text (S2T): multi-layered based security approaches for secret text content

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Multimedia Tools and Applications 2025年第15期84卷 15151-15171页

作者： Kashid, Shamal Awasthi, Lalit K. Berwal, Krishan Department of Computer Science and Engineering National Institute of Technology Uttarakhand IN Srinagar India Faculty of Communication Engineering Military College of Telecommunication Engineering IN Mhow India

In the digital world, text data is produced in an unstructured manner across various communication channels. Extracting valuable information from such data with security is crucial and requires the development of techniques in text mining, information retrieval, and natural language processing (NLP). To solve this issue, we introduce two novel approaches: keyword extraction (KE) and a multi-layered secret sharing scheme (MLSS) to provide security to extracted keywords rather than overall text documents. The KE approach encompasses a sequence of text pre-processing procedures, including tokenization, stopword removal, stemming, and bag of words representation, followed by indexing. This methodology aims to efficiently extract keywords from text datasets. For this research work, we have proposed three datasets, including text messages, whatsapp messages, and electronic mail. MLSS enhances the security of extracted textual data by leveraging text pre-processing steps. This scheme ensures better confidentiality and the non-revealment of sensitive information. Additionally, we evaluate our KE model on our dataset as well as on standard datasets. Experimental results demonstrate the effectiveness of our proposed security to text (S2T) model, which outperform existing state-of-the-art approaches. The model obtains a 100% correlation between the reconstructed text and the original text. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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Learner Phase of Partial Reinforcement Optimizer with Nelder-Mead Simplex for Parameter Extraction of Photovoltaic Models

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Journal of Bionic engineering 2024年第6期21卷 3041-3075页

作者： Jinpeng Huang Zhennao Cai Ali Asghar Heidari Lei Liu Huiling Chen Guoxi Liang Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehran***Iran College of Computer Science Sichuan UniversityChengdu610065SichuanChina Department of Artificial Intelligence Wenzhou PolytechnicWenzhou325035China

This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed *** LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,changing the state of the PRO in terms of ***,the Nelder-Mead simplex is used to optimize the best agent in the population,accelerating the convergence speed and improving the accuracy of the PRO *** comparing LNPRO with nine advanced algorithms in the IEEE CEC 2022 benchmark function,the convergence accuracy of the LNPRO has been *** accuracy and stability of simulated data and real data in the parameter extraction of PV systems are *** to the PRO,the precision and stability of LNPRO have indeed been enhanced in four types of photovoltaic components,and it is also superior to other excellent *** further verify the parameter extraction problem of LNPRO in complex environments,LNPRO has been applied to three types of manufacturer data,demonstrating excellent results under varying irradiation and *** summary,LNPRO holds immense potential in solving the parameter extraction problems in PV systems.

关键词： Partial reinforcement optimizer Learner phase Nelder-Mead simplex algorithm Parameter extraction

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Real-Time Text Extraction and Video Interpreter System 8

Real-Time Text Extraction and Video Interpreter System

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Grandhi, Aneesh Sai Umair, Syed Yaswanth, Bolla Sai Naga Ravi, Aditya Padmashree, T. Sagar, B.M. Department of Information Science and Engineering Rv College of Engineering India Rv College of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798331505462

Video interpretation systems are widely used for assisting people with visual impairments. The main goal of a video interpreter system is to help people with visual impairments. By leveraging technologies such as Text-to-Speech, text extraction and Optical Character Recognition, the description of the surrounding environment can be converted to audio and textual data present on images and documents can be extracted and read out. The Real-Time Text Extraction and Video Interpreter System extracts text from images, such as posters and documents, and provides real time descriptions of video feeds through a camera. The generated text is read as speech to aid the visually impaired person. Such a model can be used to navigate outdoor environments, guiding the users about their surroundings. © 2024 IEEE.

关键词： Optical character recognition

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Content Moderation of Generative AI Prompts

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SN computer science 2025年第4期6卷 1-28页

作者： Pardhi, Praful Computer Science and Engineering Department Shri Ramdeobaba College of Engineering and Management Nagpur India

Recent advances in Generative AI, have encouraged many industries to use generative models in their products for generating components such as images, text, audio, or video. Generative foundation models like Large Language Models and Diffusion Models are trained on large amounts of data scraped from the internet. Analysis with respect to the quality and content of this extensive data is not possible before training, which leads to generating inappropriate content that may contain profanity, violence, NSFW content, hate speech, and substance use. We cannot rely on the content the generative AI models may generate when the user prompts to generate the content. To generate fair and safe content the input prompt should be analyzed before generating the content to avoid generating unsafe content. If the prompt is unsafe the filters should avoid such a prompt or replace the improper and unsuitable words with the proper and suitable words. Here, we provide a framework for analyzing the text prompts based on the content moderation taxonomy. We define the taxonomy for content moderation and explore different language models to classify the prompt as per the moderation taxonomy. The proposed moderation taxonomy can be used to analyze user’s behavior and misuse of any generative AI application. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Content moderation Generative model Language model Large language models Prompt

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Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

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Neutrosophic Sets and Systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, University of New Mexico. All rights reserved.

关键词： Logistic regression

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Deep features and metaheuristics guided optimization-based method for breast cancer diagnosis

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Multimedia Tools and Applications 2025年第16期84卷 16683-16707页

作者： Asad, Emon Mollah, Ayatullah Faruk Basu, Subhadip Chakraborti, Tapabrata Department of Computer Science and Engineering Aliah University Kolkata700160 India Department of Computer Science and Engineering Jadavpur University Kolkata700032 India The Alan Turing Institute and University College London London United Kingdom

Breast cancer is one of the most prevalent cancer types and the second leading cause of death among women. But fortunately, early diagnosis and treatment of breast cancer reduces mortality rates and improves the quality of life significantly. In this work, a computer-aided three-stage pipeline for the diagnosis of breast cancer from mammogram images has been proposed. Firstly, an attention-aided VGG16 network has been applied for deep feature extraction from mammogram images. Then, the extracted features are passed to the adaptive beta hill climbing-aided whale optimization algorithm for the most informative feature selection. Lastly, the features from stage 2 are again evaluated by the grey wolf optimizer combined with the adaptive beta hill climbing algorithm for the selection of the best subsets of features and subsequent identification of cancers using the k-nearest neighbours classifier. The proposed three-stage method has achieved an average accuracy of 97.63% and has reduced 81.78% of the original features. Considering the fact that well annotated imaging data is a costly resource for modern data intensive deep networks, this work shows that a fairly lightweight off the shelf network like VGG-16 can be coupled with a robust feature selection policy to outperform several recent approaches which employ more data intensive learning strategies with larger networks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Mammography

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Intelligent Digital Envelope for Distributed Cloud-Based Big data Security

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computer Systems science & engineering 2023年第7期46卷 951-960页

作者： S.Prince Chelladurai T.Rajagopalan Department of Computer Science and Engineering University College of EngineeringVillupuramKakupppam605103India Department of Science and Humanities University of College of EngineeringAriyalurKavanur621705India

Cloud computing offers numerous web-based *** adoption of many Cloud applications has been hindered by concerns about data security and *** service providers’access to private information raises more security *** addition,Cloud computing is incompatible with several industries,including finance and ***-key cryptography is frequently cited as a significant advancement in *** contrast,the Digital Envelope that will be used combines symmetric and asymmetric methods to secure sensitive *** study aims to design a Digital Envelope for distributed Cloud-based large data security using public-key *** strategic design,the hybrid Envelope model adequately supports enterprises delivering routine customer services via independent multi-sourced *** the Cloud service provider and the consumer benefit from the proposed scheme since it results in more resilient and secure *** suggested approach employs a secret version of the distributed equation to ensure the highest level of security and confidentiality for large amounts of *** on the proposed scheme,a Digital Envelope application is developed which prohibits Cloud service providers from directly accessing insufficient or encrypted data.

关键词： Digital Envelope Cloud computing big data encryption decryption

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Enhanced Weld Defect Categorization via Nature-Inspired Optimization-Driven Neural Networks

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SN computer science 2024年第8期5卷 1035页

作者： Antony Vigil, M.S. Maheswari, K. Minu, M.S. Kulkarni, Gururaj L. Chandra Sekhar Reddy, L. Satishkumar, P. Haldar, Barun Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Ramapuram Chennai India Department of Computer Science and Engineering CMR Technical Campus Telangana Kandlakoya Hyderabad India Computer Science and Engineering Vardhaman College of Engineering Hyderabad Shamshabad India Department of Computer Science and Engineering (Data Science) CMR College of Engineering & amp Technology Kandlakoya(V) Hyderabad Medchal India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Mechanical and Industrial Engineering Department College of Engineering Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia

In many different sectors, the safety and dependability of welded structures are crucially dependent on the accurate identification of weld defects. To address this, we present a novel method for categorizing weld defects named as Pelican Optimization-based convolutional Bi-Long Short-Term Memory Networks (POCBi-LSTM). With this integrated strategy, weld defect categorization is intended to be more accurate and effective. To train and assess a suggested classification algorithm, a collection of 220 samples with 5various types of weld faults is utilized. The study's technique comprised denoising with a Modified Median Filter (MMF) for noise reduction and binary histogram equalization (BHE) for image enhancement. The Scale Invariant Feature Transform (SIFT) was used to extract features. The outcomes demonstrate that the proposed strategy performs greater than conventional methods and delivers superior recognition accuracy, precision, recall, and F1 scores of 92.9%, 94.5%, 89.5%, and 92.8%, respectively. Experiment findings demonstrate the effectiveness and advantage of the recommended approach in classifying weld faults. Through the use of an integrated strategy, it is hoped to increase the dependability and effectiveness of weld defect categorization by handling local pattern recognition and long-term interdependence efficiently. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Modified median filter Pelican Optimization-Based convolutional Bi-Long Short Term Memory Networks (POCBi-LSTM) Weld defects Weld structures

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Intelligent Guitar Chord Recognition Using Spectrogram-Based Feature Extraction and AlexNet Architecture for Categorization

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International Journal of Advanced computer science and Applications 2025年第4期16卷 758-767页

作者： Korade, Nilesh B. Salunke, Mahendra B. Bhosle, Amol A. Sangve, Sunil M. Joshi, Dhanashri M. Asalkar, Gayatri G. Kadu, Sujata R. Sarwade, Jayesh M. Department of Computer Engineering JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India Department of Computer Engineering PCET’s Pimpri Chinchwad College of Engineering and Research Ravet Maharashtra Pune412101 India Department of Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Bibwewadi Pune411037 India Department of Computer Engineering Terna Engineering College Maharashtra Mumbai India Department of Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Chord prediction plays a key role in the advancement of musical technological innovations, such as automatic music transcription, real-time music tutoring, and intelligent composition tools. Accurate chord prediction can assist musicians, educators, and developers in constructing tools that help in learning, playing, and composing music. Background noise and audio distortions may have an impact on chord prediction accuracy, particularly in real-world situations. Chords can have distinct voicings or finger positions on the guitar, resulting in slight variations in audio representation. This study focuses on the classification of guitar chords using techniques of deep learning. There are eight major and minor guitar chords in the dataset. They have been turned into spectrograms, chromagrams, and Mel Frequency Cepstral Coefficients (MFCC) so that features can be extracted. Various deep learning architectures, including CNN, ResNet50, AlexNet, and VGG, were employed to classify the chords. Experimental results demonstrated that the spectrogram-based AlexNet model outperforms others, achieving good accuracy and robustness in chord classification. The proposed study demonstrates the efficiency of spectrograms and advanced deep learning models for audio signal processing in music applications. By automating chord detection, this study provides beneficial resources for music learners as well as educators, enabling more efficient learning and real-time feedback during practice sessions. © (2025), (science and Information Organization). All Rights Reserved.

关键词： Deep learning

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