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Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

EAI Endorsed Transactions on Pervasive Health and Technology 2024年 10卷

作者： Dahiya, Ruby Dahiya, Virendra Kumar Deepakshi Agarwal, Nidhi Maguluri, Lakshmana Phaneendra Muniyandy, Elangovan Department of Computer Science Engineering Galgotias University Greater Noida India School of Business Galgotias University Greater Noida India Department of CSE Indira Gandhi Delhi Technical University for Women Delhi India Dept.of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Dist. Andhra Pradesh Vaddeswaram522302 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

INTRODUCTION: Parkinson's Disease (PD), a progressively debilitating neurological disorder impacting a substantial global population, stands as a significant challenge in modern healthcare. The gradual onset of motor and non-motor symptoms underscores the criticality of early detection for optimal treatment outcomes. In response to this urgency, novel avenues for early diagnosis are being explored, where the amalgamation of biomedical voice analysis and advanced machine learning techniques holds immense promise. Individuals afflicted by PD experience a nuanced deterioration of bodily functions, necessitating interventions that are most effective when initiated at an early stage. The potential of biomedical voice measurements to encode subtle health indicators presents an enticing opportunity. The human voice, an intricate interplay of frequencies and patterns, might offer insights into the underlying health condition. OBJECTIVES: This research embarks on a comprehensive journey to delve into the intricate connections between voice attributes and the presence of PD, with the aim of expediting its detection and treatment. METHODS: At the heart of this exploration is the Support Vector Machine (SVM) model, a versatile machine learning tool [1-2]. Functioning as a virtual detective, the SVM model learns from historical data to decipher the intricate patterns that differentiate healthy individuals from those with PD [3-4]. RESULTS: Through the power of pattern recognition, the SVM becomes a predictive instrument, a potential catalyst in unravelling the latent manifestations of PD using the unique patterns harbored within the human voice. Embedded within this research are the practical demonstrations showcased through code snippets [5-7]. By synergizing the intricate voice measurements with the SVM model, we envision the emergence of a diagnostic paradigm where early PD detection becomes both accessible and efficient. This study not only epitomizes the synergy of voice an

关键词： Support vector machines

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Genetic Testing of Huntington’s Disease by Facial Examination of a Mankind using AI Programs

Genetic Testing of Huntington’s Disease by Facial Examinati...

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Innovations in computer science & Software engineering (ICONICS), International Conference on

作者： S. Atruba Feroze S. Faiza Nasim Dept.of Computer & Information System Engineering NED University of Engineering & Technology Karachi Pakistan Dept. of Computer Science & I.T NEDUET NED University of Engineering & Technology Karachi Pakistan

This Genetic disorder is considered as one of the major challenges faced by Medical sciences as it is inherited by humans from one generation to another. With an increasing number of natives of the World at a higher rate, it causes a major impact on information society socially and economically during their lifetime. The citizens of Pakistan and Indian sub continents are facing genomic disease in probably high frequency as compared to other continents. Occurrence of congenital diseases in Pakistan results in higher percentages of unusual genetic diseases especially in children. After analyzing all the current scenarios, some goals and objectives were settled in order to facilitate and give general awareness to people. The objectives of systematic review are evaluation of diagnostic accuracy of AI for the identification of genome contributions to the pathogenesis of Huntington’s disease through facial probe AI to accurately identify genetic disorders. The narrative review is performed in Ned University. In this proceeding sequence, 30 cases of both Normal and Genetically affected Patients were documented in which some certain sorts of tests were performed by using facial recognition. For this FR test, at first a combo list of information of all six emotions of a human being i.e. (Happy, Sorrow, Amazed, Fear, Anger, Revulsion) is gathered in the form of mutated images, which was then later be arranged into a pseudo-random order. Now with the help of Vision Technology we have applied some CV Techniques for the extraction of large pattern sets of features. Lastly the purpose of ML and DL techniques is to receive and compare those morphed image data from the Facial Database in order to identify each of six different emotions of HD & Normal Person. This whole procedure is conducted in real time. Hence in this way we may recognize the HD genetic disorder among the people in an efficient way. The detailed procedure is explained in the Methodology section of this narrative r

关键词： Uncertainty Face recognition Genomics Feature extraction Object recognition Continents Artificial intelligence

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Deliberate Occlusion: Asymmetry in Facial Expression

Deliberate Occlusion: Asymmetry in Facial Expression

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IEEE International Conference on Knowledge Innovation and Invention (ICKII)

作者： Hung-Tse Chan Yen-Chun Chao Chih-Hsien Hsia Dept. of Electronic and Computer Engineering Nat. Taiwan Univ. of Sci. and Tech. Taipei City Taiwan Asia American International Academy New Taipei City Taiwan Dept.of Computer Science and Information Engineering National Ilan University Taiwan

Facial expression recognition (FER) has become increasingly common in research. However, region feature technologies that play an important role in this field have not been well developed. In this study, a psychology-based solution to address problems in FER of regional features was proposed. This method employs the use of deliberate occlusion for enhancement and attention module to find meaningful and important features in the regions processing features with high inter-class similarity and variation. The result of this study shows that the approach has higher accuracy than recent models and, through the use of feature heatmap visualization, the possibility of approaching FER from human facial asymmetry is raised.

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Machine learning based diagnosis and classification of sickle cell anemia in human RBC 3

Machine learning based diagnosis and classification of sickl...

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3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Sen, Bheem Ganesh, Adarsh Bhan, Anupama Dixit, Shubhra Goyal, Ayush Amity University Dept.Of Electronics and Communication Engineering Noida Utter Pradesh India Texas A and M University Frank H. Dotterweich College of Engineering Department of Electrical Engineering and Computer Science Kingsville United States

ISBN: (纸本)9780738111834

Anemia is a disease which is caused by the deficiency of red blood cells. The shape of red blood cell changes to sickle or crescent shape in sickle cell anemia disease. The manual inspection of microscopic images is very difficult and time-consuming process. In this research image processing and machine learning techniques is used to automate the process of detection of sickle cells in microscopic images then classify the RBC into three shapes: circular, elongated (sickle cell) and other shape. The microscopic image is pre-processed and Otsu thresholding technique is used for segmentation. Then, Watershed segmentation is applied to separate the overlapped cells. The geometrical, statistical and textural features are extracted from the images. The machine learning classifier random forest, logistic regression naïve baye sand support vector machine is used. This research describes the comparison among these algorithms. © 2021 IEEE.

关键词： Support vector machines

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An Improved Object Tracking and Estimating Using Yolov5 Model Based on Adaptive Kalman Filter and Efficient Inference Performance on Surveillance

SSRN

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SSRN 2022年

作者： Mishra, Abhilasha Mane, Nitin Pagare, Rajendraprasad Mishra, Aaditya Dixit, Amitabh Mishra, Sparsh Agrawal, Aditi IEEE Bombay Section Dept.of Electrical and Electronics Birla Institute of Technology. and Science Goa Pilani India Dept.of Computer Science Thakur College of Engineering and Technology Mumbai India Indus International School Pune India CSE Department MIT-World Peace University Pune India Dept.of E&TC Engineering Smt. Kashibai Navale College of Engineering Maharashtra Pune41104 India Dept.of Electronics and Computer Engineering Maharashtra Institute of Technology Aurangabad India

computer vision techniques and object detection research are widely progressive. The significant challenges faced by the video analytics and technicians are the camera alignment setup and the image quality affected due to environmental factors like obstacles with darkness, high intensity, occlusion, and other issues. The issues related to the tracking occurrence in conditional cases and the solutions are addressed in the proposed work. The issues related to the tracking occurrence in conditional cases and the solutions are addressed in the proposed work. An improved technique based on the YOLOv5 Model using an adaptive Kalman filter to track an object in real-time and estimate the labels in the classification stage is proposed. The proposed methods use Optimal precision for the processor selection and parallel distribution of the features based on the surveillance database with 97.6% accuracy with the features to the inference model delivered incremental results. © 2022, The Authors. All rights reserved.

关键词： Adaptive filtering

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Dynamic trace estimation 21

Dynamic trace estimation

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Prathamesh Dharangutte Christopher Musco Dept. of Computer Science & Engineering New York University Dept.of Computer Science & Engineering New York University

ISBN: (纸本)9781713845393

We study a dynamic version of the implicit trace estimation problem. Given access to an oracle for computing matrix-vector multiplications with a dynamically changing matrix A, our goal is to maintain an accurate approximation to A's trace using as few multiplications as possible. We present a practical algorithm for solving this problem and prove that, in a natural setting, its complexity is quadratically better than the standard solution of repeatedly applying Hutchinson's stochastic trace estimator. We also provide an improved algorithm assuming slightly stronger assumptions on the dynamic matrix A. We support our theory with empirical results, showing significant computational improvements on three applications in machine learning and network science: tracking moments of the Hessian spectral density during neural network optimization, counting triangles, and estimating natural connectivity in a dynamically changing graph.

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An Integrated Approach to Enhance Legal Information Retrieval of Sri Lankan Supreme Court Verdicts

An Integrated Approach to Enhance Legal Information Retrieva...

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Advancements in Computing (ICAC), International Conference on

作者： Asel Jayasooriya Akeel Ahamed Yomal Bandara Chamod Gavindya Dharshana Kasthurirathna Lakmini Abeywardhana Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept.of Computer Science & Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

Legal professionals and law students often grapple with the arduous task of locating prior Supreme Court rulings for argument preparation and academic study, leading to a laborious and resource-intensive process. This study introduces an innovative approach to streamline the retrieval of legal information from Sri Lankan Supreme Court verdicts. The method centers on developing a Custom Named Entity Recognition (NER) model, boasting a remarkable accuracy exceeding 90%. This model efficiently extracts crucial legal entities from Supreme Court rulings, readily available on the Ministry of Justice's Supreme Court website. The high accuracy ensures precise entity extraction, followed by systematic organization within a dedicated database. Subsequently, a knowledge graph is formed by linking recorded legal entities, reducing information retrieval time to a mere 179 milliseconds, significantly outperforming existing methods. Moreover, a BART summarization model is crafted to generate concise, accurate, and insightful summaries of Supreme Court decisions, boasting an impressive ROGUE1 score of 85%. This approach revolutionizes legal information retrieval, delivering a user-friendly platform that enhances the identification of cases and fosters a deeper understanding, ultimately elevating the quality of legal research and practice.

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APOA based Multi-scale Parallel Convolution Blocks with Hybrid Deep Learning for Gastric Cancer Prediction from Endoscopic Images

APOA based Multi-scale Parallel Convolution Blocks with Hybr...

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Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： Adithya Padthe Mahantesh Mathapati Priyanka M S Priya Nandihal Department of Information Technology University of the Cumberlands USA Dept.of CSE Amruta Institute of Engineering and Management Sciences (AIeMS) Dept. of AIML Nitte Meenakshi Institute of Technology Bengaluru Dept of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore

In terms of mortality rates, gastric cancer is second only to lung cancer. Manual gastric slice pathology examination is labor-intensive and prone to observer bias. Endoscopy of the upper digestive tract is commonly used for the screening of gastric cancer. An object identification model, a kind of deep learning, was presented as a means of automating the diagnosis of early stomach cancer using endoscopic pictures. However, difficulties were encountered while attempting to reduce the sum of false positives in the identified findings. Tumour segmentation from the preprocessed pictures was carried out in this study, which is often more challenging and crucial. The research suggests a productive approach that makes use of multi-scale parallel convolution blocks (MPCs). Multi-scale parallel convolutions (MPCs) use filters of variable sizes to extract characteristics that are relevant across a range of tumour sizes. To further aid feature extraction with fewer parameters, residual connections and residual blocks can be used. In addition, the suggested study uses an Artificial plant optimisation algorithm (APOA) to fine-tune the segmentation model's parameters without resorting to post-processing approaches. Finally, gastric cancer detection from endoscopic pictures is accomplished using a hybrid classification strategy that incorporates Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Experiments employing 1208 photos from healthy people and 533 photographs from patients with stomach cancer examined detection performance using the 5-fold cross-validation approach. These findings show promise for the suggested method's application in automated early stomach cancer diagnosis using endoscopic images.

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Deep Neural Network-Based Gender Identification for Surveillance Restroom Restriction System

Deep Neural Network-Based Gender Identification for Surveill...

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2022 International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2022

作者： Susa, Julie Ann B. Doculan, Jo Ann D. Merin, Jovencio V. Zarate, Jeddie M. Tria, Meriam L. Evangelista, Ryan Soriente Reyes, Michelle C. Southern Luzon State University Department of Computer Engineering Quezon Lucban Philippines Ifugao State University College of Computing Sciences Ifugao Philippines Technological University of the Philippines Dept.of Electrical Engineering Manila Philippines Pangasinan State University Computer Engineering Department Pangasinan Philippines Eulogio' Amang' Rodriguez Institute of Science and Technology Department of Electronics Engineering Manila Philippines Feu Nicanor Reyes Medical Foundation General Education Department Quezon City Philippines

ISBN: (纸本)9781665490870

Everyone is generally aware that there is sex separation in restrooms. This emphasizes the significance of sex-based toilet restrictions. Student's behavior and ability to learn decency will be impacted by a system that identifies a person's sex. This study offers a detecting mechanism that distinguishes between males and females. To categorize both sexes in schools, sex identification utilizing image processing was developed. The implementation of the sex identification system used the YOLOv3 technology. The study's conclusions state that the detection model used has an mAP value of 95.28 %. The implementation of the Sex Identification for Restroom Restrictions is successful since the chosen model is advised in sex identification for both males and females. © 2022 IEEE.

关键词： Deep neural networks

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Fog Computing based Privacy Preservation via Distributed Access Control System in Blockchain Technology

Fog Computing based Privacy Preservation via Distributed Acc...

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Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： Ramya Thatikonda Priya Nandihal Priyanka M S Mahantesh Mathapati PhD in Information Technology University of the Cumberlands Williamsburg USA Dept of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore Dept. of AIML Nitte Meenakshi Institute of Technology Bengaluru Dept.of CSE Amruta Institute of Engineering and Management Sciences (AIeMS) Bengaluru

The proliferation of loT -enabled gadgets means that ever more information is being created and transmitted over the web. The release of sensitive loT data, however, is a privacy breach. Sensitive information generated by loT devices poses a serious security risk since it is transmitted across an unsecured network. The storage issue may be managed with the help of an external cloud. Cloud storage has the potential to compromise users' privacy. To prevent unauthorised access and maintain confidentiality during data transmission, current systems include symmetric encryption and access restrictions. Blockchain is an exciting distributed ledger technology with the potential to safeguard stored information against manipulation by unscrupulous actors. To protect information from Internet of Things devices, this article suggests using a blockchain-based decentralised access control system. Fog computing and the idea of an alliance chain provide the basis for the suggested approach. In order to encrypt and divide data at the network's edge, fog nodes are employed. Homomorphic encryption is used on the third-party cloud to combine data that has been partitioned and encrypted. The suggested system provides dynamic and granular access control for loT data, mitigating the risk of a single point of failure in access control. Results from experiments showed that this approach effectively protects the confidentiality of data collected from Internet of Things devices.

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