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OHDLL:Optimized Hybrid Deep Learning Model for Classification of Leukaemia images

OHDLL:Optimized Hybrid Deep Learning Model for Classificatio...

2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary technology Management, IC-RVITM 2023

作者： Saju, Binju Asha, V. Tressa, Neethu Suhas, R.A. Jagesh, Shah Yash Sandhya, V. Adi Shankara Institute of Engineering & Technology Department of Computer Science - Ai Engineering Kerala Kalady India Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798350306088

A serious haematological cancer that causes mortality and morbidity in people of all ages is leukaemia. The haematologists examine bone marrow and blood to determine Leukaemia. Techniques for manual blood testing that have been in use for a long period are frequently slow and produce less reliable diagnoses. The study proposes a hybrid model to classify leukaemia cell images. Initially, images are pre-processed which includes Image resizing, Gray scale conversion and noise removal using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is compared with other studies in literature. The proposed model has obtained an F1 score of 98.98%, an accuracy of 98.20%, sensitivity of 98.9%, specificity of 98.1%, and precision of 98%. © 2023 IEEE.

关键词： Median filters

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Mobile-Based Classification and Detection of Diabetic Retinopathy Using TinyML 2

Mobile-Based Classification and Detection of Diabetic Retino...

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2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Kumar, Manish Mohan Kumar, H.P. Sultana, Husna Shweta, J.S. M S Ramaiah Institute of Technology Department of Master of Computer Applications Bangalore India Pes College of Engineering Dept of Master of Computer Applications Karnataka Mandya India Government First Grade College Department of Computer Science Karnataka Tumkur India

ISBN: (纸本)9798350314045

This paper presents a comprehensive approach to diabetic retinopathy (DR) classification and detection, encompassing the evaluation of ResNet, RegNet, and WideResNet models for DR severity assessment. Leveraging the Kaggle's APTOS 2019 dataset, we implement and analyze these models using PyTorch, highlighting their efficacy in categorizing DR severity levels. Additionally, we leverage Tiny Machine Learning (TinyML) techniques to develop a mobile-based solution, enabling real-time DR severity assessment on mobile devices. This mobile application showcases the potential of TinyML for early detection and intervention in DR cases, ultimately enhancing patient outcomes. By synergizing advanced deep learning models with TinyML, this study contributes to healthcare technology, enabling timely interventions and improved patient outcomes. © 2023 IEEE.

关键词： Equalizers

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Sentiments Analysis and Text Summarization of Medicine Reviews Using Deep Sequential Iterative Model with Attention Encoder

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Journal of Mobile Multimedia 2022年第4期18卷 1281-1300页

作者： Prabhu, B. Rajalaxmi Seema, S. Department of Computer Science and Engineering M.S. Ramaiah Institute of Technology Bengaluru India Department of Master of Computer Applications M.S. Ramaiah Institute of Technology Bengaluru India Department of Computer Science and Engineering NMAM Institute of Technology VTU Belagavi India

In this day and age of data innovation, vast amounts of data are being generated daily, much of it is useless to the general public unless it is correctly handled. This year has seen a huge increase in social networks' relevance, resulting in vast quantities of data provided by users. This vast amount of information is gathered from a variety of sources, including company websites, customer blogs, and item reviews. The text outline is probably the most significant point of view in daily life. Analysis of text sentiment is a method for assessing, summarising, and drawing conclusions about the most important content. In the field of sentiment analysis, attention methods have been crucial since they make use of sentiment lexicons to gather a huge amount of sentiment polarity *** an attention mechanism that acts as a link between linguistic information with a strong emotional component and deep learning algorithms, it may be possible to boost text sentiment significantly. In the LSTM model, word sequence addictions can be captured over the long term. For the first time, scientists have developed an attention model that combines LSTM with an incredibly deep RNN model to tackle the problem of sentiment analysis in the real world. The iterative method trains the first set of word embeddings using a Word to Vector technique. With the Word2Vec algorithm, text strings are converted into a numerical value vector and distance between words and comparative words based on meaning are calculated. Using the attention strategy has the added benefit of potentially enhancing machine learning's ability to learn sentiment representations. The attention model is more scalable and adaptable than previous approaches. The major objective of this work is to assess feelings and develop an abstracted content outline, decide on the semantic summarization of different materials and effectively process the data. © 2022 River Publishers.

关键词： attention model decoder encoder Polarity summarization

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A Novel Method for Sample Selection in Audio Stenographic Systems 6

A Novel Method for Sample Selection in Audio Stenographic Sy...

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6th International Conference on Electronics, Communication and Aerospace technology, ICECA 2022

作者： Bhatkalkar, Bhargav J. Arjunan, R. Vijaya Alok, Arunaav Sanghavi, Ria Cenitta, D. Kamath, Radhika Digipen Institute of Technology Department of Computer Science 139660 Singapore Mit Manipal Academy of Higher Education Department of Cse Manipal576104 India

ISBN: (纸本)9781665482714

This research work proposes a new method for performing sample selection in audio steganographic systems, which can also be applied to strictly frequency-based and spatial steganographic systems. This method exploits the predictability of pseudo-random number generators to generate a sequence of pseudo-random indices to develop a steganographic system that offers security without compromising the payload capacity of the carrier signal. A comparative study has also been performed among existing sample selection algorithms, namely, sequential selection and pseudo-random selection with indices embedded within the stego object. Our analysis also looks at the effect of applying RSA encryption on the source message having the stego object created. For this study, the source messages are both grayscale and RGB image files. The carrier file used is a 32-bit stereo WAV file. The metrics chosen to perform the comparative study include PSNR, Mean Square Error, Normalized Absolute Error, and Structural Content. To judge the resilience of the steganographic systems from a security point of view, the histogram analysis method is utilized to perform steganalysis. Further, the ineffectiveness of encryption in the steganographic process is also analyzed. The proposed method overcomes the vulnerabilities inherent to sequential and random sample selection methods using the unique sample selection method devised in this paper. © 2022 IEEE.

关键词： Cryptography

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Text Summarization for Kannada Text Documents: A Review 1

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Veena, R. Ramesh, D. Hanumanthappa, M. Sri Siddhartha Academy of Higher Education Tumkur572105 India Department of Master of Computer Applications Sri Siddhartha Institute of Technology Tumkur572105 India Department of Computer Science and Applications Bangalore University Bangalore560056 India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

In recent times, we have observed the emergence of Automatic Text Summarization (ATS) systems, which are now being developed not only for the English language but also for low-resource languages. The growing quantity of textual information on the Internet, including vast archives of news articles, scientific papers, legal documents, and more, has made ATS increasingly essential. Manual text summarization is known to be time-consuming, costly, and often impractical when dealing with the massive volume of text available. Since the 1980s, research scientists have been dedicated to enhancing ATS techniques. These approaches are typically categorized as extractive, abstractive, or hybrid. The extractive methodology involves selecting the most crucial sentences from the input document(s) and concatenating them to create a summary. In contrast, the abstractive methodology rephrases the content of the input document(s) into an intermediate representation, generating a summary with sentences that differ from the original text. The hybrid methodology integrates extractive and abstractive methodologies. Despite the ongoing development of various methods, it is worth noting that the summaries generated by these systems still fall short of human-generated summaries. Most research efforts have concentrated on the extractive approach, but there is a pressing need to shift more attention towards abstractive and hybrid methodologies. This research endeavour aims to offer a comprehensive survey for researcher scientists by covering various aspects of ATS, including methodologies, techniques, building blocks, methodologies, datasets, evaluation methods, and the specific challenges that need to be addressed in the context of Kannada language text. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Text processing

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A Model for Predicting Chronic Kidney Diseases Based on Medical Data Using Reinforcement Learning

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SN computer science 2024年第4期5卷 353页

作者： Nramban Kannan, Senthil Kumar Aseervatham, Joshi Moholkar, Kavita Palanimuthu, Mithun Marappan, Saranya Narendran, Muthusamy Sathar, Banu Sengan, Sudhakar Department of Computer Science & amp Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai 600123 India Department of Computer Science and Business Systems Engineering JSPM’S Rajarshi Shahu College of Engineering Savitribai Phule Pune University Pune 411033 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai 600119 India Department of Information Technology Paavai Engineering College Tamil Nadu Namakkal 637018 India Department Information Technology KCG College of Technology Tamil Nadu Chennai 600097 India Department of Master of Computer Application T. J. Institute of Technology Chennai 600100 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627451 India

Kidney diseases (KD) are a global public health concern affecting millions. Early detection and prediction are crucial for effective treatment. Artificial intelligence (AI) techniques have been used in KDP to analyze past medical records, applying patients’ Electronic Medical Record (EHR) data. However, conventional statistical analysis methods conflict with fully comprehending the complexity of EHR data. AI algorithms have helped early KDP learn and identify complex data patterns. However, challenges include training heterogeneous historical data, protecting privacy and security, and developing monitoring system regulations. This study addresses the primary challenge of training heterogeneous datasets for real-world evaluation. Early detection and diagnosis of chronic kidney disease (CKD) is crucial for improved outcomes, reduced healthcare costs, and reliable treatment. Early treatments are crucial for CKD, as it often develops without apparent symptoms. Predictive models, particularly those using reinforcement learning (RL), can identify significant trends in complex healthcare information, which standard techniques may struggle with. The study makes KDP more accurate and reliable using RL methods on clinical data. This lets doctors find diseases earlier and treat them better by looking at static and changing health measurements. Machine learning (ML) algorithms can enhance the accuracy of AI systems over time, enhancing their effectiveness in detecting and diagnosing diseases. In the current investigation, the RL-ANN model is implemented for performing enforceable CKD by assessing the outcomes of multiple neural networks, which include FNN, RNN, and CNN, according to parameters such as accuracy, sensitivity, specificity, prediction error, prediction rate, and kidney failure rate (KFR). The recommended RL-ANN method has a lower failure rate of 70% based on the KFR data. Further, the proposed approach earned 95% in PR and 70% in analysis of errors. However, the RL

关键词： Alerting systems and diagnostic assistance Chronic kidney disease Machine learning Medical history RL-ANN

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Mental Health Predictive Analysis using Multiple Machine Learning Techniques 2

Mental Health Predictive Analysis using Multiple Machine Lea...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Tressa, Neethu Asha, V. Prasad, Arpana Prathith Pramodh Kumar, D. Saju, Binju New Horiznon College of Engineering Department of Master of Computer Applications Bengaluru India Adi Shankara Institute of Engineering & Technology Department of Computer Science - Ai Engineering Kerala Kalady India

ISBN: (纸本)9798350369960

The growth in mental health issues and the demand for high-quality medical care have prompted the requirement of machine learning techniques in the psychological research area. For the better prediction of mental health complications, this work presents an up-to-date thorough evaluation of machinelearning algorithms. This article reviews the difficulties, limitations and future directions of using machine learning approaches in psychological health. By searching reliable databases, relevant studies and publications on machine learning methods were collected for predicting mental health related issues. For the completion of this work, 40 research outcomes were analyzed using screening and classification procedures. The analyzed works were categorized according to mental health conditions such as schizophrenia, bipolar disorder, depression or anxiety, PTSD, and mental health conditions affecting children. While discussing the results, the difficulties and limitations that many researchers have encountered when studying mental health related problems were also considered. The paper also encompasses specific directions for future research work that are feasible and the need for utilizing varying approaches in machine learning for psychological health disorder predictions. © 2023 IEEE.

关键词： Decision Tree Classifier Flask Deployment K Nearest Neighbor Classifier Logistic Regression Algorithm Naïve Bayes Random Forest Stacking

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Investigating Domain Adaptation for Medical Image Classification 5th

Investigating Domain Adaptation for Medical Image Classifica...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Agarwal, Ankita Seth, Jyoti Sharma, Ambrish Kumar Karthikeyan, M.P. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Master of Computer Application Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Area version goals bridge the space between supply and target domain names by remodeling statistics from the source domain to the target area without providing categorized goal facts. Medical image domain version is a critical trouble in scientific imaging obligations, consisting of most cancers prognosis, because it lets in for the switch of understanding throughout particular medical imaging modalities, obligations, and affected person cohorts. It can help address record distribution shifts and make undertaking medical photo classes more dependable and accurate. In this examination, we look into several area model techniques and benchmark their performance in the venture of the medical photo category. We examine supervised and unsupervised methods, as well as deep studying-based methods. The results of our experiments offer insights into the performance of numerous area model techniques for clinical picture class that could assist in deciding the pleasant method to use in any given medical imaging task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Identification and Segmentation of Polyp Using Deep Learning Approaches 4

Identification and Segmentation of Polyp Using Deep Learning...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Yaligar, Suman Kallur, Shanta S. Patil, Vijeeta Thotad, Puneeth N. K. L. E. Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India K. L. E. Institute of Technology Dept. of Master of Computer Applications Hubballi India

ISBN: (纸本)9798350354218

Colorectal cancer (CRC) is a major health problem, and accurate detection of polyps plays a major role in its prevention and treatment. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had issues capturing global context and lacked robustness to complex scenarios. With the rise in deep learning, more and more rigorous segmentation of medical images dependent on deep learning networks have emerged, making significant progress in this field. This paper aims to develop an efficient model for polyp detection and segmentation using machine learning techniques. The model will assist clinicians in accurately locating and segmenting polyp regions, which will help in the prevention and treatment of CRC. The work will first review traditional methods that are dependent manually on extracted features and deep segmentation algorithms, then detail benchmark datasets relevant for the topic. The two datasets Kvasir-SEG and CVC-ClinicDB are used for implementation. Specifically, we carried out a comprehensive evaluation of recent deep learning CNN methods. The model gave an accuracy with Pranet method of about 99% in polyp segmentation and identification respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Gaussian Differential Privacy Integrated Machine Learning Model for Industrial Internet of Things

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SN computer science 2023年第5期4卷 454页

作者： Lazar, Arokia Jesu Prabhu Soundararaj, Sivaprakash Sonthi, Vijaya Krishna Palanisamy, Vishnu Raja Subramaniyan, Vanithamani Sengan, Sudhakar Department of Computer Science and Engineering AI & ML CMR Institute of Technology Telangana Hyderabad 501401 India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 522502 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai 638060 India Master of Computer Applications M. Kumarasamy College of Engineering Tamil Nadu Karur 638060 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Agriculture, energy, mining, healthcare, and transportation are a few of the top industries transformed by the industrial internet of things (IIoT). Industry 4.0 mainly relies on machine learning (ML) to use the vast interconnectedness and large amounts of IIoT data that IIoT primarily drives. ML approaches are trained on confidential data generated by the IIoT environment often exposes privacy to adversarial assaults. Blockchain-based ML is established in the proposed secured IIoT research work to safeguard and enhance privacy. This research proposes a Gaussian differential privacy-integrated machine learning model (GDPIMLM), created for a scalable and controlled IIoT system, fully connected (FC) layer, implemented blockchain benefits to create a privacy-preserving mechanism while considering other limitations and reasonable time. A probability test using two altered Gaussian distributions (GD) forms the basis for defining Gaussian differential privacy (GDP). Due to a central restriction theorem for differential privacy, GDP is the primary privacy term within the family of Federal differential privacy (f-DP) policies. Ethereum is used to implement experimental evaluations. Data collections show that the recommended method enhances digital data privacy with industry-leading security without reducing performance. The performance analysis of the proposed model of secured industrial internet of things (IIoT) is studied using the accuracy of comparison with existing methods and for diverse distributed entities (DE). The proposed secured IIoT attains a minimal time consumption (TC) of 790 Sec—the highest accuracy of 92%, outperforming the existing method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Industry Industry 4.0 Learning Privacy Security Smart objects

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