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29th Pacific Symposium on Biocomputing, PSB 2024

作者： Comajoan Cara, Marçal Mas Montserrat, Daniel Ioannidis, Alexander G. Dept. of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Dept. of Signal Theory Communications Universitat Politècnica de Catalunya Barcelona Spain Dept. of Biomolecular Engineering University of California Santa Cruz Santa CruzCA United States Institute for Computational Mathematical Engineering Stanford University StanfordCA United States

ISBN: (纸本)9789811286414

The lack of diversity in genomic datasets, currently skewed towards individuals of European ancestry, presents a challenge in developing inclusive biomedical models. The scarcity of such data is particularly evident in labeled datasets that include genomic data linked to electronic health records. To address this gap, this paper presents PopGenAdapt, a genotype-to-phenotype prediction model which adopts semi-supervised domain adaptation (SSDA) techniques originally proposed for computer vision. PopGenAdapt is designed to leverage the substantial labeled data available from individuals of European ancestry, as well as the limited labeled and the larger amount of unlabeled data from currently underrepresented populations. The method is evaluated in underrepresented populations from Nigeria, Sri Lanka, and Hawaii for the prediction of several disease outcomes. The results suggest a significant improvement in the performance of genotype-to-phenotype models for these populations over state-of-the-art supervised learning methods, setting SSDA as a promising strategy for creating more inclusive machine learning models in biomedical research. Our code is available at https://***/AI-sandbox/PopGenAdapt. © 2023 The Authors.

关键词： Forecasting

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Computational Overhead Analysis for Mitigation of Content Poisoning Attack in NDN-based IoT Body Area Networks 3

Computational Overhead Analysis for Mitigation of Content Po...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Priya, M. Castro, S. Kumar, Sathesh K. Kalaivani, N. Dept. of Computer Science and Engineering E. G. S. Pillay Engineering College Nagapattinam611002 India Dept. of Information Technology Karpagam College of Engineering Coimbatore641032 India Dept. of Electronics and Communication Engineering Vivekanandha College of Engineering for Women Tiruchengode637205 India Dept. of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore641041 India

The Body Sensors Network has developed into a significant element of the Medical IoT. Massive data gathering from a resource-constrained network represents a laborious procedure in IoT equipped BSN/WBAN, creating a number of difficult problems. Conventional communication protocols may be used on IoT platforms without any alterations and provide impractical techniques for large-scale networks. The new architecture known as ICN has the potential to overcome these difficulties in huge data collection. According to this perspective, the proposed work indicates a CPA mitigation strategy for information centric WBANs. © 2023 Elsevier B.V.. All rights reserved.

关键词： Internet of things

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Self-supervised Mamba-based Mastoidectomy Shape Prediction for Cochlear Implant Surgery

arXiv

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arXiv 2024年

作者： Zhang, Yike Davalos, Eduardo Su, Dingjie Lou, Ange Noble, Jack H. Dept. of Computer Science Vanderbilt University United States Dept. of Electrical and Computer Engineering Vanderbilt University United States

Cochlear Implant (CI) procedures require the insertion of an electrode array into the cochlea within the inner ear. To achieve this, mastoidectomy, a surgical procedure involving the removal of part of the mastoid region of the temporal bone using a high-speed drill provides safe access to the cochlea through the middle and inner ear. In this paper, we propose a novel Mamba-based method to synthesize the mastoidectomy volume using only preoperative Computed Tomography (CT) scans, where the mastoid remains intact. Our approach introduces a self-supervised learning framework designed to predict the mastoidectomy shape and reconstruct a 3D post-mastoidectomy surface directly from preoperative CT scans. This reconstruction aligns with intraoperative microscope views, enabling various downstream surgical applications. For training, we leverage postoperative CT scans to bypass manual data cleaning and labeling, even when the region removed during mastoidectomy is affected by challenges such as metal artifacts, low signal-to-noise ratio, or electrode wiring. Our method achieves a mean Dice score of 0.70 in estimating mastoidectomy regions, demonstrating its effectiveness for accurate and efficient surgical preoperative planning. Copyright © 2024, The Authors. All rights reserved.

关键词： computerized tomography

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Automated Leukemia Diagnosis from Microscopic Blood Smear Images Using Adapted Inception V3

Automated Leukemia Diagnosis from Microscopic Blood Smear Im...

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Big data Analytics in Bioinformatics (DABCon), International Conference on

作者： M Vani Pujitha Srinija Srikantam Ch Sahithi Akhila Timmasarti Dept. of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350378047

ISBN: (纸本)9798350378054

Leukemia, a cancer characterized by the rapid overproduction of abnormal blood cells, presents notable diagnostic and treatment challenges. Identifying leukemia subtypes promptly and accurately is vital for devising effective treatments and improving patient outcomes. This study proposes an advanced Inception V3 model optimized for the automatic identification of leukemia from microscopic images of blood smears. By integrating cutting-edge deep learning methods and a customized model architecture, this system differentiates between Acute Lymphoblastic Leukemia (ALL), Acute Myeloid Leukemia (AML), Chronic Lymphocytic Leukemia (CLL), Chronic Myeloid Leukemia (CML), and healthy samples. The model benefits from preprocessing techniques like image resizing, normalization, and augmentation to enhance its performance. Through transfer learning, the Inception V3 model is fine-tuned on a rich dataset covering various leukemia subtypes. Experimental results demonstrate the model's high effectiveness, achieving 100% accuracy during training and 99.72% accuracy in testing, proving the system's reliability for automated leukemia detection.

关键词： Training Deep learning Adaptation models Accuracy Microscopy Transfer learning Leukemia Reliability Blood Testing

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Deep Learning-Driven Black Box Approach For Image Segmentation

Deep Learning-Driven Black Box Approach For Image Segmentati...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Nuthalapati Sudha Sai Bhargav Kasetty K. Sai Madhuri Jajala Nikitha Issac Neha Margret K. Rajakumar School of Engineering Mahindra University Hyderabad Telangana India School of Computer Science and Engineering V.I.T – University Vellore Tamilnadu India Dept. of Computer Science and Data Science BVRIT Narsapur Hyderabad Telangana India Dept. of CSE – AIML & IoT VNR VIET Hyderabad Telangana India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The diagnosis of a range of eye disorders needs to categorize the retinal vessels. computerized implementation of this process is becoming increasingly essential for automated screening systems for retinal diseases. To achieve a more accurate extraction of the retinal vessels, a new pre-processing step is proposed. These proposed pre-processes are also compared to other algorithms to assess their impact. The proposed pre-processing process consists of two phases. The first phase is the implementation and validation of the pre-processing modules, and the second phase is the implementation of these pre-processes onto the retinal vessels that were to be extracted. To achieve a significantly improved segmented vessel image, the proposed pre-process phase employs a common image-processing technique. In recent years, there has been a great deal of focus on retinal vessel identification studies, and the importance of assessing and confirming the findings of retinal vessel segmentation.

关键词： Image segmentation Analytical models Accuracy Computational modeling Retinal vessels Diseases

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Optimizing Satellite Constellations for Global Internet Coverage: A Comparative Analysis of Communication Protocols and Orbital Configuration

Optimizing Satellite Constellations for Global Internet Cove...

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Pallab Banerjee Vivek Raj Biresh Kumar Kanika Thakur Dipra Mitra Purushottam Kumar Dept. of Computer Science Engineering Amity University Jharkhand Ranchi Jharkhand India Amity School of Information Technology Amity University Jharkhand Ranchi Jharkhand India Dept. of Computer Science Engineering Galgotia University Greater Noida campus Dept. of Computer Science Engineering Amity University Jharkhand Ranchi India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

As a way to provide ubiquitous connectivity, satellite constellations are becoming more and more popular due to the growing need for worldwide internet access. In order to provide dependable and seamless worldwide internet coverage, this study investigates how satellite constellations might be optimized by comparing and contrasting communication protocols and orbital topologies. This ambitious aim involves a number of problems and complications, which are explored in the research along with the trade-offs and design considerations that must be made in order for the implementation to be effective. Two main elements form the framework of the comparative analysis. First, by considering factors like latency, data throughput, and coverage efficiency, it evaluates a range of communication protocols, including traditional geostationary satellites, Low Earth Orbit (LEO) constellations, and Medium Earth Orbit (MEO) satellites. Second, to ascertain the effect of various orbital configurations on worldwide internet coverage, the study considers satellite dispersion, orbital altitudes, and coverage patterns. By means of this study, the research endeavors to provide a valuable contribution to the current discussion on satellite constellations for worldwide internet coverage, therefore facilitating well-informed decisions about the design and implementation of these systems.

关键词： Earth Satellite constellations Adaptation models Protocols Uncertainty Low earth orbit satellites Propulsion Throughput Topology Planning

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Energy Optimization to Extend Network Lifetime for IoT based Wireless Sensor Networks 4

Energy Optimization to Extend Network Lifetime for IoT based...

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4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Shreyas, J. Deepa, H. Udayaprasad, P.K. Chouhon, Dharmendra Srinidhi, N.N. Dilip Kumar, S.M. Creencia Technologies Private Limited Indiranagar Bangalore560038 India School of Computer Science and Engineering Reva University Bangalore India Dept. of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

ISBN: (纸本)9781665401180

The issue of extending the lifetime of Wireless Sensor Networks (WSN) designed for Internet of Things (IoT) has been rigorously explored by researchers over the last two decades. IoT applications for real time are in demand for monitoring and complex analysis. In IoT, sensor nodes are powered by battery backup which is considered as energy constrain while performing various task. Maximizing the network lifetime of network nodes has become a challenging issue. In past researchers have focused on extending network lifetime of nodes through guarantee lifetime protocols, however some issues such as sensing coverage, scheduling and network connectivity are not been considered. Therefore, in this work energy efficient routing scheme (EERS) is proposed for duty cycled nodes by considering sleep/awake mode and increased data rates to achieve higher packet delivery ratio and to extend network lifetime and optimize the energy consumption of the network. Sleep/awake modes for nodes are periodically assigned by sink node based on nodes residual energy, coverage area and total active time. © 2022 IEEE

关键词： Energy efficiency

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Sedimentation Analysis on Padma River Using Machine Learning Techniques 4

Sedimentation Analysis on Padma River Using Machine Learning...

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4th International Conference on Sustainable Technologies for Industry 4.0, STI 2022

作者： Hossain Bhuiyan, Md. Ruhen Saha Roy, Niloy Roy, Monoshi Kumar Sultanul Islam Ovi, Md. Faisal, Faiz Al Aminur Rahaman, Muhammad North South University Department of Electrical and Computer Engineering Dhaka1229 Bangladesh Green University of Bangladesh Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9781665490450

This research aims to predict the sedimentation rate in Padma River and to illustrate the rate of accuracy for water and land classification based on various machine learning algorithms. Increased sedimentation can cause utterly problems for the organisms living there. Moreover, the water flow could be changed;the dept. of the water can be lessened because of river sediment deposition, making boating and recreational use extra tough. This study comprises sedimentation for the last five years and how it will influence the surrounding environment, ecology, and socio-economy in the upcoming years, predict future areas that are subject to deposition, and show the accuracy of different machine learning algorithms for water and land classification. To conduct this research, we used a dataset consisting of images of Padma River (close to Naria in Shariatpur district of Bangladesh), collected from satellite, and run image processing techniques on it. We have obtained an accuracy of 97.5% for the image of 2018 and 99.17% for the image of 2019 using K-means. On the other hand, we obtained accuracy of 91.67% for images of 2018 and 95% for the image of 2019 using a self-organizing map (SOM). However, random forest (RF) shows the best results, having an accuracy of 97.5% for the image of 2018 and 100% for the image of 2019. © 2022 IEEE.

关键词： Rivers

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Automatically Suggesting Suitable Crop from Soil Samples Using Optimized Neural Networks

Automatically Suggesting Suitable Crop from Soil Samples Usi...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Syed Nawaz Pasha D. Ramesh Sallauddin Mohmmad Gujjula Deepak School of Computer Science and Artificial Intelligence SR University Warangal India Dept. Of Computer Science and Engineering Kamala Institute of Technology and Science Huzurabad India

A soil-centric crop recommendation system recommends suitable crops utilizing vital soil attributes as input factors. This sophisticated system incorporates parameters like Nitrogen (N), Phosphorus (P), Potassium (K) levels, temperature, humidity, pH, and rainfall to suggest ideal crops for cultivation. The dataset comprises diverse crops and their specific requirements for each input parameter. We implemented an optimized neural network to provide precise crop recommendations based on the provided input values. By analyzing this data, the system provides farmers with the information necessary to make well-informed decisions regarding their choice of crops, enhancing the potential for optimal yields. Our model performed well on training and testing data with an accuracy of 0.94. These results demonstrate its effectiveness and potential as a valuable tool for agricultural decision-making. This system offers farmers tailored recommendations based on soil properties, climatic conditions, and other environmental factors.

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Optimizing Multi-Class Classification in Educational data with Ensemble Learning and data Balancing Techniques

Optimizing Multi-Class Classification in Educational Data wi...

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Intelligent data science Technologies and Applications (IDSTA), International Conference on

作者： Mohammad F. Al-Hammouri Ziad Akram Ali Hammouri Islam T. Almalkawi Ansam Lafee Dept. of Computer Engineering The Hashemite University Zarqa Jordan Dept. of Computer Science Middle East University Amman Jordan

ISBN: (数字)9798350354751

ISBN: (纸本)9798350354768

The field of education is increasingly embracing AI tools to improve student outcomes. This work aims to reduce academic failure in higher education by employing machine learning techniques to identify at-risk students early in their educational journey, enabling the implementation of supportive strategies to assist them. This study examines a dataset from a higher education institution and utilizes it to develop a classification model for predicting students’ academic performance. The problem is formulated as a multi-class classification task with three categories: Graduate, Enrolled, and Dropout, with a significant imbalance skewed toward the Graduate. To improve prediction accuracy toward the minority class, the data balancing technique SMOTE with Edited Nearest Neighbor (SMOTE-ENN) is applied. Three popular classification models—Random Forest, XGBOOST and CatBoost—are employed. The findings show that SMOTE-ENN significantly improves classification results. Moreover, XGBOOST demonstrated the highest accuracy (94.6%) in correctly identifying all classes, as evidenced by the confusion matrix evaluation, achieving the highest results compared to previous work in the literature. Implementing these models allows for accurate predictions of students’ performance and helps reduce dropout rates.

关键词： Performance evaluation Accuracy Machine learning algorithms Education Forestry Predictive models data science Prediction algorithms data models Ensemble learning

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