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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2024

作者： Kamepalli, Sujatha Veeranjaneyulu, N. Bodapati, Jyostna Devi Vignan's Foundation For Science Technology And Research Dept. Department Of Information Technology And Computer Application Guntur India Vignan's Foundation For Science Technology And Research Dept. Department Of Advnced Computer Science And Engineering Guntur India

ISBN: (纸本)9798350371567

Cataracts, a prevalent eye condition impacting vision, demand accurate and prompt identification for effective risk reduction and blindness prevention. Traditional methods of cataract diagnosis rely heavily on manual examination by ophthalmologists, which can be time-consuming and subject to human error. With the advent of digital imaging technology, there is an opportunity to enhance the efficiency of cataract detection. The deep neural network models VGG19 and ResNet-50 are recommended in this paper for automatic cataract identification on digital camera images. To optimize network performance, adjustments to loss and activation functions were made, incorporating minimal kernels, reduced training parameters, and layers. This resulted in a ResNet-50 model with significantly decreased computational cost and average operating time compared to the cutting-edge models. The proposed ResNet 50 model achieved an average accuracy of 93.41%, outperforming the VGG 19 model, which had an accuracy of 91.13%. This research contributes to the evolution of cataract detection methodologies, emphasizing the potential of deep learning models, particularly ResNet-50, in improving diagnostic accuracy and operational efficiency for enhanced eye health outcomes. © 2024 IEEE.

关键词： Digital cameras

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Malaria and Pneumonia Disease Prediction Using Deep Learning 2

Malaria and Pneumonia Disease Prediction Using Deep Learning

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Vijendra Singhal, Manoj Dwivedy, Bhoopendra Jain, Pallavi Ritika Abes Institute of Technology Department of Information Technology Ghaziabad India Gl Bajaj Institute of Technology and Management Department of Information Technology Greater Noida India School of Computer Science and Engineering Galgotia's University Greater Noida India School of Computer Application and Technology Galgotia's University Greater Noida India

ISBN: (纸本)9798350371055

Early identification, intervention, and efficient treatment planning all rely heavily on disease prediction. This study examines the methods for disease prediction based on deep learning, concentrating on pneumonia and malaria in particular. Pneumonia and malaria are two common, sometimes fatal infections, especially in underdeveloped areas. Traditional diagnostic techniques for these disorders can be time-consuming and frequently need for specialized knowledge. Therefore, it is necessary to investigate automated and reliable illness prediction methods. This study's goal is to assess how well deep learning algorithms can foresee malaria and pneumonia from medical imaging data. The study makes use of a broad dataset that includes chest X-ray pictures for the identification of pneumonia and blood smear images with malaria infection. To improve the dataset's quality and variety, preprocessing procedures including picture normalization and augmentation are used. The results of this research demonstrate the potential of deep learning algorithms for malaria and pneumonia prediction, which advances the field of medical diagnostics. These automated prediction algorithms could help medical personnel diagnose patients quickly, allowing for quick treatment to start and better patient outcomes. © 2024 IEEE.

关键词： Backpropagation CNN Deep Learning Inspection V3 Model Jupiter Python Transfer Learning

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Forensic handwriting analysis: a hybrid classification framework for writer identification in Devanagari script

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Multimedia Tools and applications 2025年 1-12页

作者： Sethi, Monika Kumar, Munish Jindal, M.K. Department of Computer Science and Application Panjab University Chandigarh India P.G. Department of Information Technology Goswami Ganesh Dutta Sanatan Dharma College Chandigarh India Department of Computational Sciences Maharaja Ranjit Singh Punjab Technical University Punjab Bathinda India Department of Computer Science and Applications Panjab University Regional Centre Muktsar India

In a period marked by swift technological progress, the task of identifying writers through handwriting holds significant importance across various domains, including forensic investigation, plagiarism detection, and historical research. The importance of this endeavor has been further magnified by the widespread influence of digitalization. This study employs a hybrid approach that integrates cutting-edge deep learning techniques, specifically Convolutional Neural Networks (CNNs), with traditional machine learning methodologies, such as Support Vector Machines (SVM) and Random Forest (RF). Deep learning techniques are particularly valuable for analyzing extensive handwriting samples because they can automatically discern complex features and patterns from large datasets. In this research, we synthesize predictions from deep learning models and employ machine learning classification for writer identification based on handwriting analysis. Central to our study is a comprehensive dataset of Devanagari characters, collected from 220 unique writers. This dataset has been thoughtfully curated to validate the methodology we have employed. Our hybrid classification approach, combining CNN with SVM and CNN with RF, achieves remarkable accuracy rates of 91.49% and 88.78%, respectively—significantly surpassing the performance of standalone SVM and RF models, which achieved accuracies of 59.52% and 59%, respectively. Through a series of comprehensive experiments, we demonstrate the enhanced effectiveness and precision of our proposed methodology in the domain of Devanagari script writer identification. The findings of this study not only underscore the potential of our methodology but also represent a substantial advancement in the fields of pattern recognition and machine learning technology. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Machine Learning to Predict Cardiovascular Disease: Systematic Meta-Analysis 6

Machine Learning to Predict Cardiovascular Disease: Systemat...

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

作者： Shuja, Mirza Qtaishat, Ahmed Mishra, Hari Mohan Kumar, Mukesh Ahmed, Bilal Lovely Professional University School of Computer Application Phagwara India Sohar University Department of Information Technology Sohar Oman

ISBN: (纸本)9798350346961

People do not have much time to think about their health in today's fast-paced society because of how busy their lives are. The overscheduling of people's lives and a widespread disregard for their health are two factors that contribute to the rise in the incidence of disease. Additionally, the great majority of people have disability that prevents them from operating properly, such as cardiovascular illness. These people are unable to operate normally because of their disabilities. According to the statistics provided by the World Health organization (WHO), cardiovascular disease is responsible for more than one-third of all deaths. Because of this, it is very necessary for anybody working in the medical industry to be able to evaluate the chance that a patient may develop cardiovascular disease. However, because hospitals and the healthcare industry produce such a vast amount of data, it may be difficult to do research owing to the sheer amount of information that is available. The use of methods that are based on Machine Learning (ML) by medical professionals has the potential to cut down on the amount of time and effort spent formulating predictions and organising data. Because of this, we have been talking about the factors that lead to heart disease as well as the methods that are involved in machine learning. We evaluated and compared the efficacy of a wide variety of well-known ML algorithms that were utilised in the experiment, and we made inferences about cardiovascular disease using these techniques. In the end, the purpose of this research is to determine whether a ML system is capable of reliably predicting cardiac illness.. © 2023 IEEE.

关键词： Forecasting

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Automated Detection of Abnormalities in Ultrasound Images Using Deep Learning

Automated Detection of Abnormalities in Ultrasound Images Us...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kumar, Amit Bhuvana, J. Kharbas, Vikas Kumar College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

Deep getting to know has been utilized in scientific imaging studies to explore challenges with diagnosing ailments and detecting anomalies. This examination focused on the automated detection of bizarre tissues in ultrasound pix. Pics must adhere to a homogeneous, properly-described, predictable anatomy for hit-deep getting-to-know applications. It looks at the proposed artificial neural network (ANN) and lengthy quick-time period memory (LSTM) based version referred to as ALunoNet to detect anomalies in ultrasound pictures. The models were skilled on a 10,000 M-mode and B-mode ultrasound pictures dataset. After ten epochs, their checking out accuracy was eighty three.16%. They look at comparing the performance of ALunoNet with other popular CNN and LSTM models. The observed effects demonstrate that ALunoNet performs higher than the same old fashions and that its overall performance is consistent even on datasets with constrained information. It has a look at highlights the capability of deep-gaining knowledge to automatize the detection of strange tissues in ultrasound pics. © 2024 IEEE.

关键词： abnormal automatized homogeneous performance ultrasound

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Future Directions of Artificial Intelligence and Machine Learning in Healthcare: A Systematic Analysis and Mapping Study 6

Future Directions of Artificial Intelligence and Machine Lea...

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

作者： Mishra, Hari Mohan Ahmed, Bilal Shuja, Mirza Qtaishat, Ahmed Kumar, Mukesh Lovely Professional University School of Computer Application Phagwara India Sohar University Department of Information Technology Sohar Oman

ISBN: (纸本)9798350346961

In order to organise previously conducted research on using machine learning in medical contexts, the authors of this paper employ a method known as systematic mapping. In order to do this, we looked through the abstracts of a variety of publications, including medical journals, healthcare periodicals, and conference proceedings, looking for the phrase 'use of artificial intelligence and machine learning in healthcare.' After doing a search on Google Scholar, which resulted in the retrieval of 500 papers, we classified these studies in accordance with their objectives, approaches, primary concerns, and illnesses. With the use of this technique, we were able to organise our findings into the five categories that are as follows: privacy and security;a framework for privacy and security;interpretable machine learning;medical image assessment;electronic health record processing;and transfer learning. In addition, we found that the evaluation of medical images is the topic that receives the most research, that interpretable machine learning and explainable artificial intelligence are becoming increasingly popular, and that most authors are primarily interested in cancer research. To restate our mission, we want to provide future generations of scholars with an accurate picture of where the field is now and where it is headed.. © 2023 IEEE.

关键词： Machine learning

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Millimeter Wave Beamforming for Massive MIMO in 6G Communication Systems

Millimeter Wave Beamforming for Massive MIMO in 6G Communica...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Shree, Ritu Verma, Pradeep Kumar Kavitha, R. Vivekananda Global University Department of Computer Science & Application Jaipur India College of Engineering Teerthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Millimeter wave beamforming for large MIMO in 6G verbal exchange systems is a promising era that could dramatically improve the overall performance and spectral efficiency of 6G networks. This generation entails the use of more than one antenna and antenna array to direct and recognize the electromagnetic power in a specific path. One of the core variations of millimeter wave beamforming in comparison with traditional more than one-antenna approaches is using fairly directional beams. This provides massive blessings in the communique link, together with improved bandwidth, improved user enjoyment, and stepped forward protection. Similarly, the use of a couple of antenna factors in this technology allows for beamforming, which could substantially lessen interference and enhance spectrum performance. Ultimately, the deployment of this era additionally calls for effective signal processing techniques, along with coding and modulation that can similarly boost the overall gadget capability. In conclusion, millimeter wave beamforming for huge MIMO in 6G verbal exchange systems can load up a huge range of possibilities and blessings, which could substantially enhance the user experience and warfare congestion in 6G networks. © 2024 IEEE.

关键词： Antenna Beamforming Dramatically Millimeter Networks Traditional

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Replication-Based Query Management for Resource Allocation Using Hadoop and MapReduce over Big Data

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Big Data Mining and Analytics 2023年第4期6卷 465-477页

作者： Ankit Kumar Neeraj Varshney Surbhi Bhatiya Kamred Udham Singh Department of Computer Engineering and Application GLA UniversityMathura 281406India. College of Computer Sciences and Information Technology King Faisal UniversityHofuf 31982Saudi Arabia School of Computing Graphic Era Hill UniversityDehradun 248002India.

We live in an age where everything around us is being *** generation rates are so scary,creating pressure to implement costly and straightforward data storage and recovery *** model functionality is used for creating a cluster parallel,distributed algorithm,and large *** MapReduce strategy from Hadoop helps develop a community of non-commercial use to offer a new algorithm for resolving such problems for commercial applications as expected from this working algorithm with insights as a result of disproportionate or discriminatory Hadoop cluster *** results are obtained in the work and the exam conducted under this job;many of them are scheduled to set schedules,match matrices’data positions,clustering before determining to click,and accurate mapping and internal reliability to be closed together to avoid running and execution *** output and proponents have been implemented,and the map has been used to reduce the *** execution input key/value pair and output key/value pair have been *** paper focuses on evaluating this technique for the efficient retrieval of large volumes of *** technique allows for capabilities to inform a massive database of information,from storage and indexing techniques to the distribution of queries,scalability,and performance in heterogeneous *** results show that the proposed work reduces the data processing time by 30%.

关键词： big data hadoop mapreduce resource allocation query management

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Study of Adaptive Nonlinear Signal Processing Structures for Underwater Acoustic Systems

Study of Adaptive Nonlinear Signal Processing Structures for...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kaushik, Neeraj Prabhu, A. Kaushik, Harshita College of Engineering Teerthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

This takes a look at and explores the use of adaptive nonlinear sign processing structures for underwater acoustic systems. The focus of the research is on how such structures can improve the accuracy of acoustic alerts underwater by adapting to various environmental conditions. Particularly, the research will analyze various acoustic sign distortion additives caused by nonlinear distortion, as well as the effectiveness of diverse adaptive nonlinear signal processing systems for reducing these effects. Sign processing strategies will include both adaptive thresholding and adaptive nonlinear time-frequency transformations. The studies will examine the overall performance of these systems in phrases of signal fidelity, in addition to the impact on cognitive complexity. The effects of the studies will inform capability enhancements and packages of nonlinear sign processing systems for underwater acoustic structures. © 2024 IEEE.

关键词： Performance Research Thresholding Transformations Underwater

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Understanding Social Networks Using Machine Learning Sentimental Analysis Algorithms

Understanding Social Networks Using Machine Learning Sentime...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Goswami, Garima Varun Somanna, M.N. Inzimam College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

Social networks are dynamic and complex structures that depend closely on human interplay. Machine learning and nostalgic evaluation algorithms can be used to gain insights into the shape and dynamics of these networks. Through those techniques, patterns of connections between people may be exposed, in addition to sentiment associated with subjects or entities. This know-how can help understand higher social networks over more than a few subjects, including fitness, politics, and the stock market. Moreover, such techniques may be implemented to leverage community effects to optimize the overall performance of advertising and marketing techniques. © 2024 IEEE.

关键词： advertising associated evaluation nostalgic techniques

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