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IoT based Smart wheelchair For Disabled Person

IoT based Smart wheelchair For Disabled Person

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Lalit Kumar Ajay Shriram Kushwaha Kartikeya Sherawat Zishan Siddique Department of Computer Application School of Computer Science & Engineering IILM University Greater Noida Uttar Pradesh India Department of Computer Science & Applications Sharda School of Enginering & Technology Sharda University Greater Noida Uttar Pradesh India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Smart wheelchairs are revolutionizing mobility for disabled individuals, offering enhanced independence and improved quality of life. These advanced devices incorporate cutting-edge technologies like obstacle avoidance systems, voice control, and self-navigation capabilities, transforming the traditional wheelchair into an intelligent mobility solution. Obstacle avoidance systems utilize sensors to detect and avoid obstacles, ensuring safe navigation in complex environments. Voice control enables users to operate the wheelchair hands-free, providing greater convenience and control. Self-navigation capabilities allow the wheelchair to autonomously navigate to pre-programmed destinations, reducing user burden and enhancing independence. Smart wheelchairs also offer connectivity features, enabling remote monitoring and control by caregivers. This provides an added layer of safety and support for users, ensuring timely assistance when needed. The integration of these advanced technologies into smart wheelchairs is transforming mobility for disabled individuals, empowering them with greater independence, safety, and control over their movement. As smart wheelchair technology continues to evolve, the possibilities for enhanced mobility and improved quality of life for disabled individuals are limitless.

关键词： computer vision Three-dimensional displays Navigation Wheelchairs Speech recognition Software Safety Older adults Collision avoidance Intelligent sensors

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Leveraging Machine Learning for Early Detection of Cardiovascular Diseases

Leveraging Machine Learning for Early Detection of Cardiovas...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Anurag Shrivastava Meenakshi Maindola Rakesh Kumar H Pal Thethi S P Sreeja N Sirisha Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of Computer Engineering & Applications GLA University Mathura Lovely Professional University Phagwara India Master of Computer Application New Horizon College of Engineering Bangalore Department of Computer Science & Engineering MLR Institute of Technology Hyderabad Telangana

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

In order to enhance patient outcomes, rapid and precise detection approaches are needed for cardiovascular diseases (CVDs), which are among the top causes of death globally. By examining a wide range of demographic and clinical data, this study investigates how machine learning approaches could improve the early diagnosis of CVDs. We explore how many different machine learning methods could identify different heart diseases. Decision trees, support vector machines and more, are included in these algorithms. By using feature selection and optimization methods we want to strengthen these models' prediction abilities and make them more resilient. The dataset used by this research includes all of the medical information of patients as they include all the pertinent biomarkers such as age, blood pressure, cholesterol levels and etc. The results suggest that machine learning models, especially ensemble methods like Random Forest and gradient boosting, are indeed able to predict the risk of CVD better than conventional diagnostic strategies. Results suggest that early risk assessment using machine learning embedded within healthcare processes is a reliable and non-invasive approach. This study has found machine learning to have promise for change and further research into the practical use of these models to revolutionize treatment of cardiovascular diseases.

关键词： Support vector machines Accuracy Computational modeling Machine learning Medical services Predictive models Blood pressure Cardiovascular diseases Risk management Random forests

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Research on named entity identification method based on medical field 3

Research on named entity identification method based on medi...

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3rd International Conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Zhou, Runnan Li, Mingxuan Ruan, Peiguang Tong, Wang Gong, Bing Lv, Wei Zhuhai College of Science and Technology Alibaba Cloud Big Data Application College Guangdong Zhuhai519000 China Beijing LongChuangYueDong Network Technology Co. Ltd Beijing100000 China Guangdong Polytechnic of Science and Technology Computer Engineering Technology College Guangdong Zhuhai519000 China

ISBN: (纸本)9781510663473

With the development of Internet technology, more and more scholars are applying computer technology to research in the medical field. In this paper, we will investigate the named entity recognition method. Under the study of BERT-CRF model, we propose a named entity recognition model based on multi-task pre-training model with adversarial learning and network sharing and apply it to entity recognition in medical field with the aim of improving the accuracy of entity recognition in medical field. The model introduces multi-task joint learning and adversarial learning modules to improve the entity boundary effect and solve the noise problem of word boundary information, while achieving the purpose of information enhancement. On the CMeEE (Chinese Medical Entity Extraction) dataset, the model showed a significant improvement in accuracy, recall, and F1 score. © 2023 SPIE.

关键词： Learning systems

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Cancer Genomics: Identifying Biomarkers for Early Detection and Treatment

Cancer Genomics: Identifying Biomarkers for Early Detection ...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Husain, Saif O. Kaur, Amandeep Chitra, S. Sharma, Vivek Aminovna, Marupova Manzura Rajesh, G. The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Mohali India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Ies University Department of Information Technology M.P. Bhopal India Fergana Medical Institute of Public Health Fergana Uzbekistan Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350390759

Genomic sciences, specifically with regards to cancer, has provided optimism in early diagnosis, treatment options by pointing to the molecular basis of cancer. This work aims at defining biomarkers for cancer diagnosis and treatment - or in other words, discovering novel biomarkers for early detection and treatment of cancer using multi-omics data from TCGA and ICGC. Ing data obtained through genome, transcriptome, and proteome studies , we revealed novel biomarkers corresponding to different types of cancer. The aforementioned datasets were processed with the help of random forests and artificial neural networks because these machine learning techniques delivered high biomarker recognition rates. This will incorporate, biomarkers such as BRCA1 for breast cancer and EGFR for lung cancer, showing high sensitivity and specificity levels. It also tackles issues such as tumor heterogeneity and presents a cardiovascular treatment plan that involves multiple disciplines. Interestingly, the assessment of genomics for cancer diagnosis and treatment has outlined such possible avenues for further investigations and practical use in the future. It is positioned in the rapidly expanding field of personalized oncology;this research seeks to improve patients' lifestyles via the early and accurate identification of cancer. © 2024 IEEE.

关键词： Lung cancer

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Medical CT Image Super-resolution Algorithm based on GAN

Medical CT Image Super-resolution Algorithm based on GAN

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computer, Vision and Intelligent Technology (ICCVIT), International Conference on

作者： Saihu Tian Taile Peng Zhongan Wang Zhengfeng Li School of Computer Science and Technology Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei Normal University Huaibei China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

Computed Tomography (CT) images are essential for visualizing the internal structures of the human body and are widely applied in the diagnosis of various diseases. However, during the processes of generating, storing, and transmitting medical CT images, the clarity of these images can be degraded by non-human factors, resulting in issues such as reduced resolution and blurred texture details, thereby compromising image fidelity. In this study, we present an approach utilizing Generative Adversarial Networks (GANs). that incorporates a grayscale value loss as an additional constraint while maintaining the original loss function constraints. We conduct experiments for 4x super-resolution reconstruction. To evaluate the reconstruction performance, we compare the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index (SSIM) of the reconstructed images. The experimental results demonstrate ,which the proposed method exhibits promising performance in both quantitative metrics and visual quality.

关键词： Visualization PSNR Computed tomography Computational modeling Superresolution Noise Gray-scale Generative adversarial networks Image reconstruction Medical diagnostic imaging

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Developing Algorithms for Predicting Gene-Disease Associations

Developing Algorithms for Predicting Gene-Disease Associatio...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Kaur, Jasdeep Maheshwari, B. Adnan, Myasar Mundher Kumhar, Bhupchand Maheswari, G. Sriyashwanth, A. Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Mohali India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Hindustan Institute of Technology and Science Department of Information Technology Chennai603103 India Saveetha Institute of Medical and Technical Sciences Department of Bioinformatics Tamilnadu Chennai India

ISBN: (纸本)9798350390759

It is evident that identifying gene-disease relationships is essential to elucidate disease genomics and progression as well as aiding in developing targeted therapies. This work aims at training and testing various machine learning approaches for predicting these relations, as well as considering various datasets, and their integration involving multi-omics data. The process of data collection has been carried out from Genomic, Transcriptomic, and Clinical data stored in various data repositories and then preprocessed for feature selection and fine-tuning of the model along with the development of Supervised, Unsupervised, and Integrated Models. The models were built and tested using cross-validated data sets;model efficiency was assessed with respect to accuracy, precision, recall and F1-score. The findings showed that the models from the second group of hybrids obtained a systematically higher accuracy than models from the first and subsequent groups, especially when analyzing multiple types of 'omics data at once. Similar deployments on entirely separate datasets ensured the model's reliability and portability across different contexts. Furthermore, interpretability features and visualization modules were included into the software to improve assessment and interactivity for users and scholars. This research will be beneficial for the field of genomics since it proposes tools that will help in the better understanding of disease and the identification of genes involved in particular diseases, making it possible to start the process of targeting important genes in order to develop cures for such diseases. The study results preaciously highlight the significance of applying integrated strategies of data and analytic methodology to improving the accuracy of the prediction and implementing the personalized medicine concept. © 2024 IEEE.

关键词： Genome

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Potato Leaf Disease Detection Method is Based on the Support Vector Machines

Potato Leaf Disease Detection Method is Based on the Support...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Animesh Srivastava Bhupender Singh Rawat Vikash Sawan Vishal Shiv Ashish Dhondiyal Department of Computer Science & Engineering Graphic Era Hill University Dehradun College of Smart Computing COER University Roorkee Department of Computer Engineering & Applications GLA University Mathura Uttar Pradesh School of Computer Application Lovely Professional University Jalandhar Punjab Department of Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

Potato production is challenged by plant diseases whose adverse effects are a reduction in the yield of the crops, and which have undesirable economic implications. Recognizing these diseases is of utmost importance since control should be applied. This paper describes a method for diagnosing diseases of the potato using Support Vector Machine (SVM) classification. A SVM is an ML algorithm that is effective in classification tasks such as classifying potato leaves in terms of their diseases. The results show that the proposed method is useful and works rather well for detecting potato plant leaf diseases. In this paper, the SVM method emphasizes detecting various potato plant leaf diseases for efficient intervention before the late stage of visual assessment for microbial-introduced disease. The main aim of this research is to assist in overcoming the limitations of existing agricultural practices in speeding up the diagnosis and control of diseases affecting potato plants. So the SVM-based methods can help farmers in making timely decisions, reducing economic losses, and increasing agricultural productivity. With linear SVM, the accuracy stands at 93.1%. Nevertheless, when non-linear SVM is employed, the accuracy rises to 95.6%.

关键词： Support vector machines Productivity Economics Visualization Accuracy Crops Real-time systems Usability Diseases Resilience

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Deep Distributed Learning and Spatial-Temporal Features for Facial Depression Detection 7

Deep Distributed Learning and Spatial-Temporal Features for ...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Arunachalam, Sripriya Ghate, Shubhangi N. Kopperundevi, N. Balamurugan, M.S. Kanagajothi, D. Srimathi, S. SRM Institute of Science and Technology Computer Science and Application Vadapalani Campus College of Science and Humanities India Ramrao Adik Institute of Technology Electronics and Telecommunications Engg. D. YiPatil University Maharashtra Navi-Mumbai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Panimalar Engineering College Department of ECE India Vel Tech Rangarajan Dr Shangunthala R&D Institute of Science and Technology Department of Mathematics India Saveetha School of Engineering SIMATS Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350340600

One of the most frequent and debilitating mental illnesses is major depression. Video-based identification of depression using facial expressions has been proposed by a number of deep learning architectures. These structures are frequently modeled for regression with Euclidean loss to foretell the degree of depression. The ordinal correlation between depressive symptoms and exposure to facial depictions is not explored, and the models' resilience to noisy and ambiguous labeling is constrained as a result. Famous deep architectures have shown guarantee in facial depression acknowledgment, but they still lack proper discriminative power because of problems with a lack of labeled depression information for deep transfer learning, large variations in facial expression between people who have identical symptom severity, and subtle variations in facial expression between varying depression severities. This study models the challenge of facial depression identification as one of the learnings to distribute labels, and this study offers a deep combined label distribution and metric learning approach to this problem. Using distribution learning, this research study provides a deep learning architecture for predicting depressive symptoms. The proposed deep learning on spatiotemporal feature improves the process of depression prediction because the two learning modules work together to improve the feature's representation ability and discriminatory strength. The results of empirical evaluation of the proposed method on publicly available datasets prove the system efficacy. © 2023 IEEE.

关键词： Deep learning

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Intrusion Detection Based on Deep Belief Network and Stacking Method 3

Intrusion Detection Based on Deep Belief Network and Stackin...

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3rd International Conference on computer Communication and Network Security, CCNS 2022

作者： Wang, Jia Bao Miao, Xiang Hua Li, Xiang Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and Technology Yunnan Kunming650500 China

ISBN: (纸本)9781510660113

After data preprocessing, the feature dimension of NSL-KDD dataset increases from 42 dimensions to 122 dimensions. High dimensional data will make it more difficult for the model to learn the characteristics of the data, and there will be a lot of redundant data in the data set. Therefore, this paper uses the deep belief network to reduce the dimension of the characteristics of the intrusion detection data set after data preprocessing, and uses the stacking algorithm as the classifier to construct the intrusion detection model. Through comparative experiments, it is proved that the model has good performance in the four evaluation indexes of accuracy, precision, recall and F1 score, and effectively improves the performance of intrusion detection model. © 2022 SPIE.

关键词： Intrusion detection

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Improving Beamforming in 5G Networks Using Reinforcement Learning

Improving Beamforming in 5G Networks Using Reinforcement Lea...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Mahalakshmi, V. Alabdeli, Haider Devi, G Sandhya Kaur, Simranjit Kharya, Asheesh Bhatt, Vimal G. Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Aditya Engineering College Department of Computer Science & Engineering India Chandigarh Group of Colleges Department of Computer Application Chandigarh Engineering College Punjab Mohali India Ies College of Technology Department of Electronics & Communication Engineering M.P. Bhopal India A. D. Patel Institute of Technology Cvm University Department of It Gujarat India

ISBN: (纸本)9798350390759

This paper presents a study on training of Reinforcement Learning (RL) for balancing of beamforming in 5G network. Major challenges in giving directions in traditional beamforming collapse in dynamic surroundings: Beamforming which plays an essential role in increasing the strength and quality of a signal. RL provides a solution by allowing system to discover that actions that lead to a favorable outcome in the environment. The research process will involve first having a 5G network emulation, designing a DQN for beamforming, and finally having a comparison with traditional methods or other established algorithms. Effects that are observed on signal-to-noise ratio and throughput, and latencies further showed that RL based beamforming yield enhanced results. This paper also captures the possibilities of RL in handling aspects of 5G network and the development of wireless communication. © 2024 IEEE.

关键词： Reinforcement learning

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