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Sickle Cell Classification Using Deep learning 3

Sickle Cell Classification Using Deep Learning

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3rd IEEE international conference on intelligent Technologies, CONIT 2023

作者： Goswami, Neelankit Gautam Goswami, Anushree Sampathila, Niranjana Bairy, G. Muralidhar Department of Biomedical Engineering Manipal576140 India D.Y. Patil Deemed to Be University School of Biotechnology and Bioinformatics Navi Mumbai400614 India

ISBN: (纸本)9798350338607

Artificial Intelligence has been a boon to healthcare for quite a long time. While AI has the potential to assist in several domains, blood smear analysis has several challenges that need to be addressed to ensure the accuracy, interpretability, safety, and ethical use of AI in this context. Proper validation, overlapping of cells, data availability, addressing biases, interpretability, regulatory compliance, workflow integration, and ethical considerations are important aspects that must be carefully considered when using AI in blood smear analysis. One of the lesser-tackled problems through Artificial Intelligence is the classification of sickle cells. Sickle cell disease is a genetic disorder affecting the hemoglobin resulting in a reduced supply of oxygen to the entire body. Currently, there is no cure for sickle cell disease, and treatment is focused on managing symptoms and preventing complications. Manual identification and classification of sickle cells in blood smear images can be time-consuming and prone to human error. Hence, there is a need for automated methods to classify sickle cells and tackle these problems. This paper discusses a deep learning model to detect the presence of sickle cells in the blood, thereby classifying them. The model focused on here is ResNet50 giving a test accuracy of 93.88%. © 2023 IEEE.

关键词： Deep learning

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PhysioNet: Vision based Human Exercise Action Recognition leveraging Transfer learning 3

PhysioNet: Vision based Human Exercise Action Recognition le...

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3rd IEEE international conference on intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Subramanian, R. Raja Pavan Kumar Gatikoppu, Sai Phanindra Chitikina, Venkata Krishna Jana, Poornesh Naga Mani Teja Gollapalli, Sri Venkata Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India

ISBN: (纸本)9798350361186

Technology-driven solutions are increasingly important tools for those who want to maintain an active lifestyle in a time when personal health and fitness are given more attention than ever before. This study offers a fresh solution to the problem of workout scoring and identification. The main goal is to develop an automated system that can recognize and evaluate a wide variety of workouts done in video recordings with an emphasis on improving exercise form, posture, and safety. To accomplish reliable exercise identification, the suggested system makes use of Convolutional Neural Networks (CNNs), YoLo and transfer learning. The model is given access to a large amount of visual data thanks to the collection and preprocessing of an extensive dataset of workout videos. The customized CNN architecture provides insights into the feature extraction and learning processes, in addition to visualizations. The creation of an automated exercise detection and scoring system that tackles the issues of exercise diversity, real-time processing, accuracy, generalization, and model interpretability is one of the research's major accomplishments. This initiative provides users with quick feedback to enhance the quality and safety of their exercises by fusing cutting-edge deep learning algorithms with real-world applications in fitness monitoring. The outcomes show a notable improvement in activity identification accuracy, supporting the system's potential to assist people in leading healthier, more active lives. This study advances the nexus between technology and fitness by providing new opportunities for personalized fitness tracking, real-time exercise coaching, and more extensive uses in health and wellbeing. © 2024 IEEE.

关键词： Video recording

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Automatic audit system based on intelligent mining of audit work order text 3

Automatic audit system based on intelligent mining of audit ...

引用

3rd international conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Zhao, Guoyi Liu, Mingming Gu, Li Ren, Haiyang Chang, Le He, Jingsong Wang, Zongwei Jiang, Xinyue State Grid Customer Service Center State Grid Corporation of China Tianjin China School of Information Science and Technology University of Science and Technology of China Hefei China Marketing Service Center of Anhui Province State Grid Corporation of China Hefei China School of Microelectronics University of Science and Technology of China Hefei China

ISBN: (纸本)9781510680449

With the rapid development of electricity marketing business, the volume of business and data for power grid companies has significantly increased. Traditional manual auditing methods are unable to meet modern management demands. According to statistics, the manual misjudgment rate during the quality inspection process of audit work orders has reached 17.8%. This article establishes an intelligent audit model for audit work orders based on text mining technology. Firstly, pre-trained models are used to generate sentence vectors. Then, TextRank algorithm and MMR algorithm are integrated to generate summaries. Subsequently, using a Transformer model with incorporated sparse attention mechanism, the generated summaries are used as inputs for binary classification to obtain audit results. Experimental results demonstrate that the proposed audit model for work orders can achieve automated processing and fine-grained analysis of marketing audit monitoring tasks, effectively improving the efficiency and accuracy of the auditing process. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Commerce

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Using GANs to Improve the Accuracy of Machine learning Models for Malware Detection 23rd

Using GANs to Improve the Accuracy of Machine Learning Model...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Simion, Ciprian-Alin Balan, Gheorghe Gavrilut, Dragos Teodor Alexandru Ioan Cuza Univ Fac Comp Sci Iasi Romania Bitdefender Lab Iasi Romania

ISBN: (纸本)9783031217524;9783031217531

The increase of cyber-attacks and new malware in the last decade led to the usage of various machine learning techniques in security products. While these techniques are designed to improve accuracy, some practical constraints (such as lowering the false positive rate) often influence the selected model. This paper focuses on how various generative adversarial networks can be used to improve the average detection rate and reduce the false positives for a given neural network, by altering the training set. The result of this paper is a technique that can be used to reduce the number of false positives while preserving or in some cases increasing the detection rate.

关键词： Malware Threats Adversarial machine learning False positives Detection GAN

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Research on intelligent agricultural decision-making system based on Bayesian optimization 3

Research on intelligent agricultural decision-making system ...

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3rd international conference on Artificial Intelligence and Computer engineering, ICAICE 2022

作者： Yan, Xiaoying Software Engineering Institute of Guangzhou Guangdong Guangzhou510300 China

ISBN: (纸本)9781510663473

Along with social development, the problems of insufficient rural labor and mismatch between labor intensity and economic returns have become a greater obstacle to rural development;therefore, the development of agricultural intelligence to improve agricultural productivity will become the new direction of modern agricultural development. Based on Internet technology, intelligent agriculture adopts digital technologies such as intelligent perception, network transmission and big data processing to provide decision basis for agricultural planting, production and pest control, or directly deploy decision information to automated farm equipment, so as to use agricultural resources reasonably and efficiently. However, the development of smart agriculture is constrained by the low accuracy of automation control and decision information. In this paper, we optimize the threshold value of each agricultural production decision data through machine learning algorithm, use Bayesian optimization to learn the agricultural production environment and crop growth data, iteratively optimize the threshold value to get the global best value, improve the accuracy of automation control and reduce the risk of agricultural production decision, and effectively improve agricultural The economic returns of agriculture can be effectively improved. © 2023 SPIE.

关键词： Machine learning

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A Comprehensive Study on Computer Aided Approaches for Multiclass Neurological Disorder Classification 3

A Comprehensive Study on Computer Aided Approaches for Multi...

引用

3rd international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2023

作者： Binu Rajan, M.R. Kavitha, K. Jayasudha, J.S. Annamalai University Department of Computer Science and Engineering Chidambaram India Central University of Kerala Department of Computer Science and Engineering Kasargod India

ISBN: (纸本)9798350306989

Visualizing anatomical structure benefits from medical imaging and analysis. However, employing basic imaging methods is challenging and ineffective. Brain abnormalities diagnosis and classification manually is especially a tedious task. Additionally, the approach currently in use experiences inter-observer variability while interpreting images. Diagnosis must be done with extreme care because even a small human error can have serious effects. An MRI can be used as a non-invasive method to find tumors. Early disease diagnosis can be accomplished by Computer Aided Diagnosis (CAD) systems, potentially improving survival odds and reducing the requirement for MRI analysis by a specialist. CNN have demonstrated to be quite efficient at finding tumors in brain MRIs. The automated diagnosis of brain MRI images using a unique computer-aided diagnosis method is described here. In recent decades, neuroimaging, especially MRI, has played a pivotal role in advancing our understanding of brain function and the various illnesses that can impact. Accurately identifying such illnesses from acquired neuroimaging data poses significant challenges due to the similarities in disease characteristics. This study analyses and evaluates the effectiveness of deep learning methods to identify neurological diseases. This study focuses on Alzheimer's, Picks, Huntington's, Cerebral Carcinoma and Tumors. © 2023 IEEE.

关键词： Magnetic resonance imaging

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An Approach to Authenticity Speech Validation Through Facial Recognition and Artificial Intelligence Techniques 23rd

An Approach to Authenticity Speech Validation Through Facial...

引用

23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Faria, Hugo Rodrigues, Manuel Novais, Paulo Univ Minho Braga Portugal

ISBN: (纸本)9783031217524;9783031217531

Since all times, humans tend to adapt their speech, in terms of authenticity, according to the moments specific needs, making some statements or claims about something that do not correspond to reality, in short, lying about some matter. Identifying such moments has always been a challenging task, not at all times successful, and requiring external artefacts, with scarce availability and inducing stressful situations. With recent advances in hardware technology, making enormous computational power available in our hands through smartphones and fast network technologies, and with Artificial Intelligence evolution, namely Deep learning, it seems possible to achieve the goal of validating speech authenticity, with smartphones, using facial recognition to detect signs of untruthful speech. This paper presents a framework to achieve this goal.

关键词： Artificial intelligence Deep learning Facial recognition Deception detection

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ML_Alzheimer: Alzheimer Disease Prediction Using Machine learning 3

ML_Alzheimer: Alzheimer Disease Prediction Using Machine Lea...

引用

3rd international Mobile, intelligent, and Ubiquitous Computing conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Farag, Mohamed S Gomaa, Ibrahim A Abd-Elghany Zeid, Magdy Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia

ISBN: (纸本)9798350306231

Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of Alzheimer's disease, researchers have proposed several distinct methods. To create more effective learning strategies, however, a deeper comprehension of Alzheimer's research is necessary. The condition is diagnosed but not until a later stage. As a consequence, the disease's progression or its symptoms' onset can be slowed if detected earlier. This article uses two datasets and machine learning techniques to predict Alzheimer's disease based on psychological characteristics such as age, gender, number of visits, and Mini-Mental State Examination (MMSE). We ultimately focused on the masculine gender and characteristics. The following results (accuracy) were discovered: SVM: 84%, KNN: 79%, and Random forest: 86%. Neural network: 86%;Naive Bayes: 83%. © 2023 IEEE.

关键词： Support vector machines

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Advancements in Sensor Technology and Computer Vision for Enhanced Road Safety and Anomaly Detection using Machine learning Approach 3

Advancements in Sensor Technology and Computer Vision for En...

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3rd international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2023

作者： Jesi, V. Elizabeth Jenitha Mary, L. Saranya, S. Poonguzhali, C. Mohanaprakash, T.A. Saranya, K. School of Computing College of Engineeering Srm Institute of Science and Technology Department of Networking and Communications Kattankulatur Chennai India Dhanalakshmi College of Engineering Department of Cse Chennai India Rajalakshmi Institute of Technology Department of Aids Chennai India S A Engineering College Department of Ece Chennai India Panimalar Engineeering College Department of Cse Chennai India Panimalar Engineeering College Department of Aids Chennai India

ISBN: (纸本)9798350306989

Development of sensor technology has significantly increased recently. To combat the high rates of fatalities and expenditures connected with injuries caused by traffic, sensor technology and computer vision have achieved considerable advancements. To improve road safety, an intelligent system has been created with the aim of spotting anomalies in the road and providing driving support. Utilizing a combination of accelerometer sensors, vibration motion sensors, gyroscopic sensors, and GPS technology, this system is smoothly integrated into cars. A comprehensive and evidence-based systematic review focused on Computer Vision applications for automated Road Defect and Anomaly Detection is still lacking, despite the fact that prior studies and applications of computer vision have concentrated on particular aspects of road hazards. The system can identify several types of road irregularities based on the current location of the vehicle by using machine learning techniques to examine sensor data. Additionally, it gathers and compares data from other vehicles at the same GPS locations to confirm the presence of road problems like speed bumps or potholes. The GPS application provides access to a cloud-based repository where all the acquired data is safely kept. The technology notifies drivers ahead of time, advising them to slow down as they approach GPS spots that have previously been identified as having road abnormalities. The results of this poll could provide the scientific community with insightful information that would help improve traffic conditions and safety in general. experience. © 2023 IEEE.

关键词： Anomaly detection

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A Study of Deep learning Models in Diagnosis and Prediction of Chronic Diseases 3

A Study of Deep Learning Models in Diagnosis and Prediction ...

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3rd international conference on Issues and Challenges in intelligent Computing Techniques, ICICT 2022

作者： Begum, Sayyada Hajera Vidyullatha, P. Koneru Lakshmaiah Education Foundation Dept of Computer Science and Engineering A.P Vaddeswaram India

ISBN: (纸本)9781665482684

With the introduction of big data and its incredible advancement in image procurement devices, transformation of medical data into valuable knowledge has become an important challenge in the area of bioinformatics. The medical images procured require huge analysis and diagnosis of images using AI techniques like Machine and Deep learning (ML, DL) that yields automated diagnosis solutions. Deep learning methods can provide optimized and precise solutions for medical image diagnosis and can be an important methodology for imminent health care applications. Deep learning in biomedical image classification, automated image classification can aid in effective and early treatment of various diseases thus reducing mortality and morbidity rate. This paper gives an introduction to Deep learning models and reviews some contemporary deep learning models and their application in detecting various diseases. © 2022 IEEE.

关键词： Image classification

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