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Blood Cell Classification and Counting in Hematology through Deep Learning

Blood Cell Classification and Counting in Hematology through...

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Sustainable Expert systems (ICSES), International Conference on

作者： P. Ananthi K Nirmala Devi T. E. Ramya P. Shanmugapriya T. Aradhya Department of Computer Technology Kongu Engineering College Perundurai Erode Tamil Nadu Department of Computer Applications Madanapalle Institute of Technology & Science M.Sc Software Systems Kongu Engineering College Perundurai Erode Tamil Nadu

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Medical diagnosis is one of the areas that has been greatly influenced by the progress of computer vision technologies. This study presents a unique approach for the detection and counting of blood cells in hematology: You Only Look One algorithm version 7 (YOLOv7). YOLO is designed for real-time object detection, making it ideal for applications such as blood vessel detection where speed is critical. The architecture allows the model to process images faster than other recognition models, such as R-CNN or SSD, which is very important for situations that require fast results, such as analysis automatic blood at clinical sites. The purpose of the proposed method is to overcome the shortcomings of conventional methods and to accurately and quickly identify blood cells in mechanical images. The YOLOv7 model has been utilizedbecause it can detect objects in real-time with highprecision and speed. The methodology for bloodcell counting involves the use of YOLOv7 togenerate bounding boxes around each detectedblood cell in an image, where the count of these bounding boxes directly corresponds to the number of cells. This advancement in hematology not onlyimproves blood cell analysis efficiency but also expedites the process of diagnosing and planningtreatment in various medical situations. This paper details how YOLOv7 was modified and adjusted to meet the specific requirements of hematologicalimage analysis, including training models, preparing datasets, and evaluating performance. The study also addresses the potential impact of this technology on clinical workflows, highlightinghow it might help medical practitioners make decisions more quickly and intelligently. Hematology analysis systems including YOLOv7 are able to improve laboratory diagnostics and help patients by providing better care and results. This workconcludes by demonstrating the revolutionary potential of YOLOv7 in blood cell identificationand counting in hematology, paving the way for accurate and better

关键词： Analytical models Accuracy Computational modeling Cells (biology) Real-time systems Reliability Object recognition Medical diagnosis Blood Medical diagnostic imaging

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Studying the effects of feature selection approaches on machine learning techniques for Mushroom classification problem

Studying the effects of feature selection approaches on mach...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Siddique, Abu Bakar Abu Bakar, Muhammad Ali, Raja Hashim Arshad, Usama Ali, Nisar Ul Abideen, Zain Ali Khan, Talha Ijaz, Ali Zeeshan Imad, Muhammad Gik Institute of Engg. Sciences & Tech Faculty of Computer Science & Engg Khyber Pakhtunkhwa Pakistan Ai Research Group Gik Institute of Engg. Sciences & Tech Faculty of Computer Science & Engg Khyber Pakhtunkhwa Pakistan University of Regina Faculty of Electronic Systems Engg Regina Canada Applications & Nanostructure Leioa BCMaterials Basque Center for Materials Basque Country Spain Univ. of Europe of Applied Sciences Dept. of Tech & Software Engg Berlin Germany

ISBN: (纸本)9798350315974

Feature selection is a critical factor affecting the performance of optimization algorithms. Without proper feature selection, optimization algorithms may suffer from slow convergence, overfitting, increased computational requirements, and longer execution times. On the other hand, omitting important features can lead to loss of relevant information, decreased accuracy, bias, and increased vulnerability to noise and outliers. This study investigates the use of genetic algorithms as a feature selection technique for a classification problem, specifically the mushrooms classification problem. Random forest is employed as the machine learning classifier, and genetic algorithms are compared with correlation as the feature selection method. The results show that genetic algorithms achieve higher accuracy, precision, recall, and F1-score compared to correlation-based feature selection. However, genetic algorithms have limitations in their applicability to specific optimization problems, the need for proper parameter setup, and longer convergence times. Despite these drawbacks, genetic algorithms prove to be superior to other feature selection techniques, particularly correlation-based approaches. This study highlights the importance of selecting appropriate feature selection techniques for optimization algorithms to improve their performance and achieve better results. In addition, this study explored the performance of various machine learning approaches on the complete mushroom dataset with 22 features and shows that genetic algorithms with feature selection as the most accurate method. © 2023 IEEE.

关键词： Genetic algorithms

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Enhancing Content-Based Image Retrieval for Lung Cancer Diagnosis: Leveraging Texture Analysis and Ensemble Models 7

Enhancing Content-Based Image Retrieval for Lung Cancer Diag...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Jayabalaji, K.A. Laxmaiah, Bagam Thatipudi, Jayendra Gopal Chandraprabha, K. Badhe, Pravin Balaram, Allam Sri Krishna Arts And Science College Department of Software Systems Tamilnadu Coimbatore India Cmr Technical Campus Department of Cse Telangana Kandlakoya India University of North Texas Department of Computer Science DentonTX76203 United States Bannari Amman Institute of Technology Department of Information Technology Tamilnadu Sathyamangalam India Sinhgad College of Pharmacy Swalife Biotech Department of Pharmacology Pune India Mlr Institute of Technology Department of Computer Science And Engineering Telangana Hyderabad500043 India

ISBN: (纸本)9798350371406

In image retrieval systems, many search engines rely on text-based methods, extracting information from descriptions and tags to retrieve relevant images. However, this conventional approach often overlooks the optical or semantic information present in photographs, leading to errors in the retrieved results. To address this issue, it is essential to consider content-based information such as intensities, edges, and pixel values when designing image retrieval systems, especially in domains like medicine. This study focuses on developing a Content-Based Image Retrieval (CBIR) system for the domain of medicine, with a particular emphasis on lung cancer detection. Early identification and treatment of lung cancer are critical for patient outcomes, making suitable computer-Aided Diagnosis (CAD) systems essential for assisting clinicians. The proposed ensemble model incorporates various feature extraction methods, including image processing techniques and wavelet transform, to harness the valuable information from images. These features are then fed into a deep learning model to assess their effectiveness in image retrieval tasks. By combining machine learning and image processing techniques, this work presents a comprehensive CBIR system capable of efficiently retrieving relevant medical images. Such advancements in image retrieval technology hold significant promise for aiding medical professionals in diagnosing and treating diseases like lung cancer promptly. © 2023 IEEE.

关键词： computer aided diagnosis

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Analyze and Compare the Public Cloud Provider Pricing Model and the Impact on Corporate Financial 7th

Analyze and Compare the Public Cloud Provider Pricing Model ...

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7th International Conference on Microelectronics and Telecommunication Engineering, ICMETE 2023

作者： Singh, Jaideep Pal, Souvik Sarkar, Bikramjit Selvi, H. Adhikari, Saurabh Madhumathi, K. Akila, D. Liverpool Business School Liverpool John Moores University Liverpool United Kingdom Department of Management Information Systems Saveetha College of Liberal Arts And Sciences Saveetha Institute of Medical And Technical Sciences Chennai India Kolkata India Department of Computer Science and Engineering JIS College of Engineering Kalyani India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed University Chennai India School of Engineering Swami Vivekananda University Kolkata India Department of Computer Application Anna Adarsh College for Women Chennai India Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Deemed University Chennai India

ISBN: (纸本)9789819995615

In order to better understand the relative costs of IaaS, PaaS, and SaaS, it is recommended that this study collect data on these three categories of cloud computing services. The purpose of this research is to analyze how much money is saved or lost while adopting cloud services for different-sized enterprises. Its purpose is to identify and quantify outcomes that are beneficial to society, whether they be monetary or otherwise. Additionally, we want to provide a complete picture of cloud services' economic effect by itemizing their benefits and drawbacks. The collected data is stored in a secure location and treated ethically to prevent unauthorized access. Then, any conflicts interesting are investigated thoroughly and resolved. The researcher may be certain that the study is being conducted in a responsible and moral manner if they follow these criteria. The research summed up with the conclusion that every device is capable of running any SaaS application. It could be a smartphone, laptop, desktop computer, or tablet. In this manner, businesses can better protect their data, and employees may take advantage of the flexibility of accessing services from any device so long as they have their secure line key. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Web services

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A Data-Driven Analytical Approach on Digital Adoption and Digital Policy for Pharmaceutical Industry in India 7th

A Data-Driven Analytical Approach on Digital Adoption and Di...

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7th International Conference on Microelectronics and Telecommunication Engineering, ICMETE 2023

作者： Rana, Anup Sarkar, Bikramjit Parida, Raj Kumar Adhikari, Saurabh Anandha Lakshmi, R. Akila, D. Pal, Souvik Liverpool Business School Liverpool John Moores University Liverpool United Kingdom Department of Computer Science and Engineering JIS College of Engineering Kalyani India School of Computing Science and Engineering Galgotias University Greater Noida India School of Engineering Swami Vivekananda University Kolkata India Department of Computer Application Anna Adarsh College for Women Chennai India Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Deemed University Chennai India Department of Management Information Systems Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai India Kolkata India

ISBN: (纸本)9789819995615

The Indian pharmaceutical industry has witnessed exponential growth over the past few decades to become the third largest globally in volume terms and a leading supplier of affordable generics. This paper provides a comprehensive assessment of the industry’s evolution, structure, performance metrics, priorities, challenges and future outlook. Secondary data from government reports, industry associations, company documents and academic literature is synthesized to develop a holistic perspective. The analysis indicates that innovation, research productivity, clinical trials, ethical practices and sustainability shape the industry’s development. Strategic priorities include strengthening R&D capabilities, expanding exports, leveraging digital technologies across operations, ensuring robust supply chains and adopting global quality and ESG standards. Investments in innovation have risen but remain below international benchmarks. While generics account for majority revenues, emerging segments like biosimilars and complex drugs offer growth opportunities. Digital adoption is increasing with data analytics and AI gaining prominence. However, issues related to regulations, pricing control, IP protection, skill shortages and environmental impact need resolution through appropriate reforms and public–private collaboration. India’s vision to become a leading pharmaceutical innovation and manufacturing hub would require reinforcing policy frameworks around IP protection, pricing, regulatory efficiency, skill building and technology adoption. Overall, the industry holds immense potential for beneficial growth but realizing it would necessitate concentrated efforts from stakeholders to build organizational capabilities, foster collaboration and imbibe global best practices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Supply chains

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Ali, Raja Hashim Shabbir, Muhammad Zeeshan Abideen, Zain Ul Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Bakar, Muhammad Abu Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models. © 2023 IEEE.

关键词： Genetic algorithms

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Product Recommendation System using Deep Learning based Recurrent Neural Network

Product Recommendation System using Deep Learning based Recu...

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International Conference on computer Communication and Informatics (ICCCI)

作者： S. Shanthi K. Nirmaladevi N. Mathiarasi As. Krithika Tharani A. Monisha R. Kuppuchamy N. Krishnamoorthy Department of Computer Science & Engineering Kongu Engineering College Perundurai India Department of Computer Applications PSNA College of Engineering and Technology Dindigul Tamilnadu India Department of Software Systems and Engineering Vellore Campus Vellore Institute of Technology India

Today’s marketing strategies place a high priority on comprehending customer sentiments. It will not only give businesses a better understanding of how their clients view their goods and/or services, but it will also give them suggestions on how to enhance their offerings. We make an effort to comprehend the relationship between various factors in customer reviews on an online store for women’s clothing. The proposed system also classifies each review, according to, whether it recommends the product in question or not, and whether it has a positive, negative, or neutral sentiment. With the exception of review titles and review texts, we used univariate and multivariate analyses on datasets features to accomplish these goals. Additionally, we have implemented a bidirectional recurrent neural network (RNN) with a long-short term memory unit (LSTM) for sentiment and recommendation.

关键词： Deep learning Recurrent neural networks Filtering Clothing Informatics Recommender systems Business

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A Novel Method for Pneumothorax Diagnosis and Segmentation Using Deep Convolution Neural Network

A Novel Method for Pneumothorax Diagnosis and Segmentation U...

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International Conference on computer Communication and Informatics (ICCCI)

A pneumothorax is the accumulation of air in the pleural space between the chest and lungs. Air that is in the lungs during breathing can leak out of the lungs and become trapped between the chest and lungs in the thoracic cavity, causing chest pain, difficulty breathing, and ultimately the complete collapse of the taut lungs. pneumothorax. Therefore, early diagnosis and treatment should be started. Neural networks have proven effective in image classification tasks, and many researchers believe that training neural network models can detect radiologist-diagnosed chest abnormalities such as pneumonia and pneumothorax. is proving. Moreover, we utilize various techniques, including Stochastic Weight Averaging (SWA), aggressive data augmentation, and learning rate schedule. We use the chest x-ray dataset provided by the 2019 SIIM-ACR In this study, a ResNet-34 neural network is trained on a data set of chest x-rays containing over 10,675 images to create a model that predicts the presence of pneumothorax from chest *** suggested system receives a Dice Similarity Coefficient (DSC) of 0.8410

关键词： Training Image segmentation Schedules Pulmonary diseases Neural networks Lung Stochastic processes

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eShadow+: Mixed Reality Storytelling Inspired by Traditional Shadow Theatre 46

eShadow+: Mixed Reality Storytelling Inspired by Traditional...

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46th IEEE Annual computers, software, and applications Conference, COMPSAC 2022

作者： Moumoutzis, Nektarios Christoulakis, Marios Xanthaki, Chara Maragoudakis, Yiannis Christodoulakis, Stavros Paneva-Marinova, Desislava Pavlova, Lilia Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia Bulgaria School of Electrical & Computer Eng. Technical University of Crete Lab. of Distr. Multimedia Inf. Systems & Applications Chania Greece Computer Science Teacher Directorate of Secondary Education Chania Greece Academy of Sciences Laboratory of Telematics Bulgarian Sofia Bulgaria

ISBN: (纸本)9781665488105

eShadow is a digital storytelling platform inspired by traditional Shadow Theatre. It enables the creation of digital stories within a project-based approach that may start from scenario development and include the creation of digital puppets and sceneries, the set-up and recording of story scenes and the final assembly of a digital story. This paper presents how eShadow can be enhanced to solve the problem of creating mixed reality installations to offer rich learning experiences in informal learning settings. This enhanced version is eShadow+ and it is evaluated via two installations which are described and compared. The evaluation results demonstrate the effectiveness of the approach thus offering new learning opportunities that are aligned with current trends in the use of mixed reality technologies. © 2022 IEEE.

关键词： Theaters

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Multimodal Transformer Fusion for Emotion Recognition using Facial and Voice Data

Multimodal Transformer Fusion for Emotion Recognition using ...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Rana Veer Samara Sihman Bharattej R Hyba AbdulJaleel K. Subhashini Ramya Srikanteswara Narendra Chennupati Doctorate of Business Administration National Louis University FL USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Applications Nandha Engineering College Erode India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Hindsight Software Solutions Inc Frisco TX

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Nowadays, emotion recognition has become a crucial aspect of human-computer interaction for personalized and empathetic interactions. However, the existing Convolutional Neural Network (CNN) depended in a single modality which led to limited accuracy. Hence, this research proposes Multimodal Transformer Fusion (MTF) to recognize human emotions by combining the strengths of audio and image data. Initially, the input data is collected from Facial Emotion Recognition 2013 (FER2013) and Berlin Database of Emotional Speech (EmoDB) datasets which consists of facial images and speech signals. Then, the image data is preprocessed with histogram equalization and wiener filter to improve image quality and the signal data are preprocessed with Butterworth Bandpass Filter (BBF) respectively. After that, the Linear Binary Patterns (LBP) and Mel-Frequency Cepstral Coefficients (MFCC) are employed to extract the features from image and signal data respectively. Then, Convolutional Recurrent Neural Network (CRNN) and Bi-Directional Gated Recurrent Unit (Bi-Gru) are introduced to classify the emotions into seven types. Finally, the proposed MTF is employed to integrate the classified emotions and provides a final prediction of recognized emotions. From the results, the proposed MTF achieved better results when compared to CNN with Long Short-Term Memory (CNN-LSTM) in terms of accuracy (97.28%) respectively.

关键词： Emotion recognition Accuracy Image recognition Convolution Computational modeling Speech recognition Feature extraction Transformers Convolutional neural networks Integrated circuit modeling

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