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arXiv 2024年

作者： Min, Aehong Wang, Xuan Correia, Rion Brattig Rozum, Jordan Miller, Wendy R. Rocha, Luis M. Donald Bren School of Information & Computer Sciences University of California IrvineCA United States Luddy School of Informatics Computing & Engineering Indiana University BloomingtonIN United States Instituto Gulbenkian de Ciência Oeiras Portugal Dept. of Systems Science & Industrial Engineering Binghamton University BinghamtonNY United States School of Nursing Indiana University IndianapolisIN United States

Objective — To (1) identify health-related terms used on social media posts that do not precisely match the health-related meaning of terms in a biomedical dictionary, (2) decide which terms need to be removed in order to improve the quality of the dictionary in the scope of biomedical text mining tasks, (3) evaluate the effect of removing imprecise terms on such tasks, and (4) discuss how human-centered annotation complements automated annotation in social media mining for biomedical purposes. Materials and Methods — We used a dictionary built from biomedical terminology extracted from various sources such as DrugBank, MedDRA, MedlinePlus, TCMGeneDIT, to tag more than 8 million Instagram posts by users who have mentioned an epilepsy-relevant drug at least once, between 2010 and early 2016. A random sample of 1,771 posts with 2,947 term matches was evaluated by human annotators to identify false-positives. Frequent terms with a high false-positive rate were removed from the dictionary. To study the effect of removing those terms, we constructed knowledge networks using the refined and the original dictionaries and performed an eigenvector-centrality analysis on both networks. OpenAI’s GPT series models were compared against human annotation. Results — Analysis of the estimated false-positive rates of the annotated terms revealed 8 ambiguous terms (plus synonyms) used in Instagram posts, which were removed from the original dictionary. We show that the refined dictionary thus produced leads to a significantly different rank of important terms, as measured by their eigenvector-centrality of the knowledge networks. Furthermore, the most important terms obtained after refinement are of greater medical relevance. In addition, we show that OpenAI’s GPT series models fare worse than human annotators in this task. Discussion — Dictionaries built from traditional clinical terminology are not tailored for social media language and can bias results when used in biomedical infe

关键词： Eigenvalues and eigenfunctions

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Outage Performance of Cross-Packet HARQ

arXiv

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arXiv 2022年

作者： Feng, Jiahui Shi, Zheng Yang, Guanghua Miridakis, Nikolaos I. Ma, Shaodan Tsiftsis, Theodoros A. The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China The Dept. of Informatics and Computer Engineering University of West Attica Aegaleo 12243 Greece The State Key Laboratory of Internet of Things for Smart City University of Macau China

As opposed to hybrid automatic repeat request with incremental redundancy (HARQ-IR) that all the resources are occupied to resend the redundant information, cross-packet HARQ (XP-HARQ) allows the introduction of new information into retransmissions to substantially exploit the remaining resources. This letter provides a profound investigation into the outage performance of XP-HARQ. In particular, the exact outage expression of XP-HARQ is derived if the maximum number of transmissions is two, and tight outage bounds are obtained for more than two transmissions. Moreover, the asymptotic outage analysis of XP-HARQ in the high signal-to-noise ratio (SNR) regime is carried out not only to simplify the outage expression, but also to show that full time diversity is achievable by XP-HARQ. The simulation results are eventually presented for verifications. © 2022, CC BY.

关键词： Signal to noise ratio

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Bioinformatics approach to identify the core ontologies, pathways, signature genes and drug molecules of prostate cancer

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informatics in Medicine Unlocked 2023年 37卷

作者： Hossain, Md. Bipul Adhikary, Apurba Islam, Imtia Hossain, Mohammad Amzad Uddin, K.M. Aslam Bristy, Sadia Afrin Rahman, Md Habibur Dept. of Information and Communication Engineering Noakhali Science and Technology University Noakhali 3814 Bangladesh Mandy Dental College & Hospital Dhaka Bangladesh Dept. of Information and Communication Technology Islamic University Bangladesh Bioinformatics and Biomedical Research Network of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Islamic University Kushtia 7003 Bangladesh Center for Advanced Bioinformatics and Artificial Intelligent Research Islamic University Kushtia 7003 Bangladesh

Prostate cancer (PoC) is currently one of the most frequent cancers in males. Though a lot of work has been done on it, the biomarkers in the progression of prostate cancer have not been fully identified. In this research, we used the GSE70466 and GSE104131 for RNA-Seq gene expression datasets to reveal the important biomarkers and related pathways of prostate cancer. Furthermore, Differentially Expressed Genes (DEGs) were extracted from the dataset with the R statistical language tools, and to investigate the functional enrichment of the DEGs, an online repository Enrichr was used. Then, another database, STRING was used in this work to generate the protein-protein interaction (PPI) network and represented the network using the well-known Cytoscape software. After that, a Cystoscape plug-in MCODE identified clusters from the PPI network. In the end, some drug compounds that may be useful in the therapy of PoC have been revealed. A total of 83 common DEGs (43 upregulated and 40 downregulated) were exposed using some statistical criteria. Overexpressed DEGs were engaged in the metabolism of glutamine family amino acids and alpha-amino acids, while underexpressed DEGs were mostly involved in the regulation of the transmembrane receptor protein serine/threonine kinase signaling pathway and positive regulation of potassium ion transport. In addition, the overexpressed DEGs were highly involved with Arginine and Proline metabolism, whereas the underexpressed DEGs were connected with Proteoglycans in cancer, according to the KEGG pathway analysis. A total of 154 nodes and 2068 connections were used to construct the PPI. Using the connectivity method, we conclude 20 genes (INS;VEGFA;CDH1;IGF1;ITGB1;IGF1R;CAV1;ESR1;KDR;TGFB1;CD44;MMP9;PIK3R1;SHC1;IGF2;SMAD4;TGFBR1;ENG;IRS1;VWF) as hub genes. Finally, we identified and validated two important biomarkers as well as certain pharmacological compounds that could be effective in the therapy of PoC. © 2023 The Authors

关键词： Biomarkers Differentially expressed gene Drug molecules Gene ontology Pathway analysis Prostate cancer Protein-protein interaction network

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Chaotic American zebra search optimization algorithm for benchmark challenges

Chaotic American zebra search optimization algorithm for ben...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Vikram Kumar Kamboj Ardhala Bala Krishna Chaman Verma Maria Simona Raboaca Constantin Filote Maria Stepanova Schulich School of Engineering University of Calgary Alberta Canada Dept. of Electrcical and Electronics Engineering Malla Reddy Engineering College for Women Hyderabad India Dept. of Media and Edu. Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava Romania Engineering Business and Management Faculty Bauman Moscow State Technical University Russia

In this research, we presented a modification to the recently developed swarm premised American zebra search optimization algorithm (AZOA) using chaos. The particular Singer mapping was proposed by the so-called chaotic AZOA and was already known to perform better in optimization. Twelve uni, multi modal benchmark functions, three-bar truss and ten bar truss optimal structural designs were tested. The chaotic AZOA with Singer map would perform more effectively, more consistently, and quicker than the classical AZOA and other recent metaheuristics in optimization, according to the results, which also supported the viability of the modifications. It was discussed if the chaotic AZAO could be optimized with the original AZOA, and the chaotic AZOA was suggested for use in applications for actual engineering challenges.

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Quality Grading of Beef using Deep Convolutional Neural Network

Quality Grading of Beef using Deep Convolutional Neural Netw...

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HCT Information Technology Trends (ITT)

作者： Aarav Sharma Tanupriya Choudhury Tanmay Sarkar Madhusmita Mohanty Nikunj Bansal Sachi Nandan Mohanty Informatics Cluster School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand India CSE Dept. University of Petroleum and Energy Studies (UPES) CSE Dept. Graphic Era Hill University Dehradun Uttarakhand India Department of Food Processing Technology West Bengal State Council of Technical Education Govt. of West Bengal Malda Polytechnic Malda India VIT-AP School of Business (VSB) VIT-AP University Amaravati Andhra Pradesh India School of Computer Science & Engineering VIT-AP University Amaravati Andhra Pradesh India

Beef is one of the most widely consumed meat, being an organic substance it is prone to degradation over time. In our paper we have proposed a Convolutional Neural Network model to grade a given sample of Beef and predict its freshness as good or bad. We have proposed a CNN model architecture along with a data (image) pre-processing pipeline that can be used to classify a given Beef sample. We have done comparative analysis between different pre-processing pipelines as well as other CNN based classification models and after training have achieved 99.63% test accuracy.

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Optimizing Brain Tumor Segmentation with Advanced Superpixel Techniques in Conjunction with Deep Learning Methods

Optimizing Brain Tumor Segmentation with Advanced Superpixel...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Kanegonda Ravi Chythanya Aruna S Jayakumar Karuppaiah S. Benjamin Arul Piyush Kumar Pareek School of Computer Science & Artificial Intelligence SR University Warangal India Dept. of Computational Intelligence SRMIST Chennai India Department of Biomedical Engineering KIT- Kalaignarkarunanidhi Institute of Technology Coimbatore India School of Engineering and Technology Jeppiaar University Chennai India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India

Brain tumor segmentation in medical imaging is crucial for precise diagnosis and treatment planning. To address these issues, our research leverages deep learning techniques and introduces an innovative approach that combines various key components. Our methodology includes preprocessing, superpixel segmentation, feature extraction, and the use of a Convolutional Neural Network (CNN) to achieve precise brain tumor segmentation. This approach is driven by the necessity for improved accuracy and efficiency, especially with diverse clinical data and tumor types, like the challenging BraTS 2018 dataset, consisting of 1070 T1W-CE MRI images. This approach using metrics such as the Dice score, sensitivity, and specificity, which demonstrate the superior performance of our superpixel model in brain tumor segmentation. This proposed method yields exceptional results, with a Dice score of 97.06%, sensitivity of 99.05%, and specificity of 99.83%, highlighting its accuracy and effectiveness in tumor segmentation when compared to existing methods such as UNet, Kapur entropy, and rough set theory. This research significantly advances the state of the art and offers great promise for enhancing medical image analysis in this critical field.

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As a mechanism that achieves the goal of decision management

As a mechanism that achieves the goal of decision management

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2021 International Conference on Information science and Communications Technologies, ICISCT 2021

作者： Raximov, Nodir Quvondikov, Jurabek Dusanov, Xurshid Daminova, Barno Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Dept. of Software of Information Technologies Tashkent Uzbekistan Applied Mathematics and Informatics Jizzakh Branch of National University of Uzbekistan Named after Mirzo Ulugbek Jizzakh Uzbekistan Karshi State University Methods of Teaching Computer Science Karshi Uzbekistan

ISBN: (纸本)9781665432580

Working with different data depending on the appearance and content of the object. Formation of types of objects and content of processes within the framework of human activity. Based on this, the control mechanism is also different. Management effects in the learning system are understood as the transfer of knowledge in the subject area to the student using the methods and tools formed by the teacher in the educational environment, taking into account all the factors generated in this process. The control system in this view is a simple system with a nonlinear feedback section. It combines complexes consisting of technical, software, telecommunications and methodological tools used in the educational process. They can be used to form alternative solutions not only by delivering parts of the training materials, but also by collecting and processing the information needed to manage the learning process. The training method is chosen according to the requirements of the learning environment and depends on the topic. If the chosen method is accepted, the training part is provided, which is fixed (question-answer or in another form), which makes it possible to work independently. © 2021 IEEE.

关键词： Learning systems

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Embeddings of planar quasimetrics into directed 1and polylogarithmic approximation for directed sparsest-cut

arXiv

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arXiv 2021年

作者： Kawarabayashi, Ken-Ichi Sidiropoulos, Anastasios National Institute of Informatics 2-1-2 Hitotsubashi Chiyoda-ku Tokyo Japan Dept. of Computer Science University of Illinois at Chicago United States

The multi-commodity flow-cut gap is a fundamental parameter that affects the performance of several divide & conquer algorithms, and has been extensively studied for various classes of undirected graphs. It has been shown by Linial, London and Rabinovich [20] and by Aumann and Rabani [5] that for general n-vertex graphs it is bounded by O(log n) and the Gupta-Newman-Rabinovich-Sinclair conjecture [13] asserts that it is O(1) for any family of graphs that excludes some fixed minor. We show that the multicommodity flow-cut gap on directed planar graphs is O(log3n). This is the first sub-polynomial bound for any family of directed graphs of super-constant treewidth. We remark that for general directed graphs, it has been shown by Chuzhoy and Khanna [11] that the gap is Ω(n1/7), even for directed acyclic graphs. As a direct consequence of our result, we also obtain the first polynomial-time polylogarithmicapproximation algorithms for the Directed Non-Bipartite Sparsest-Cut, and the Directed Multicut problems for directed planar graphs, which extends the long-standing result for undirectd planar graphs by Rao [22] (with a slightly weaker bound). At the heart of our result we investigate low-distortion quasimetric embeddings into directed 1. More precisely, we construct O(log2n)-Lipschitz quasipartitions for the shortest-path quasimetric spaces of planar digraphs, which generalize the notion of Lipschitz partitions from the theory of metric embeddings. This construction combines ideas from the theory of bi-Lipschitz embeddings, with tools form data structures on directed planar graphs. © 2021, CC BY.

关键词： Polynomial approximation

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Wital: A COTS WiFi Devices Based Vital Signs Monitoring System Using NLOS Sensing Model

TechRxiv

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TechRxiv 2021年

作者： Zhang, Xiang Gu, Yu Yan, Huan Wang, Yantong Dong, Mianxiong Ota, Kaoru Ren, Fuji Ji, Yusheng The School of Computer and Information Hefei University of Technology China I+ Lab School of Computer Science and Engineering University of Electronic Science and Technology of China China The Dept. of Sciences and Informatics Muroran Institute of Technology Japan The National Institute of Informatics Tokyo101-8430 Japan

Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras;however, the former could affect sleep quality, while the latter often present privacy concerns. To address these shortcomings, we propose Wital, a contactless vital sign monitoring system based on low-cost and widespread commercial off-the-shelf (COTS) Wi-Fi devices. There are two challenges that need to be overcome. First, the torso deformations caused by breathing/heartbeats are weak. How can such deformations be effectively captured? Second, movements such as turning over affect the accuracy of vital sign monitoring. How can such detrimental effects be avoided? For the former, we propose a non-line-of-sight (NLOS) sensing model for modeling the relationship between the energy ratio of line-of-sight (LOS) to NLOS signals and the vital sign monitoring capability using Ricean K theory and use this model to guide the system construction to better capture the deformations caused by breathing/heartbeats. For the latter, we propose a motion segmentation method based on motion regularity detection that accurately distinguishes respiration from other motions, and we remove periods that include movements such as turning over to eliminate detrimental effects. We have implemented and validated Wital on low-cost COTS devices. The experimental results demonstrate the effectiveness of Wital in monitoring vital signs. © 2021, CC BY.

关键词： Wireless local area networks (WLAN)

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Network security analysis for signal strength based packet filitering 3

Network security analysis for signal strength based packet f...

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3rd International Conference on Intelligent Sustainable Systems, ICISS 2020

作者： Sundaram, B. Barani Rajkumar, P. Ananthi, M. Sravan Kumar, D. Vijaykumar, Janga Karthika, P. Bule Hora University College of Informatics Department of Computer Science Bule hora Ethiopia KGiSL Institute of Technology Dept of Computer Science and Engineering Coimbatore Tamilnadu India Institute of Technology Dept of IT KGiSL Coimbatore Tamilnadu India Balaji Institute of Technology Science Dept of CSE Warangal India Bule Hora University College of Informatics Dept. of Information Technology Bule Hora144 Ethiopia Kalasalingam Academy of Research and Education Department of Computer Applications TamilNadu India

ISBN: (纸本)9781728170893

Signal Strength based position check is all the more encouraging when contrasted with other Sybil assault discovery methods since it is lightweight and doesn't need centralization. Hubs can locally decide their areas through got signal quality varieties and illuminate neighbors about area refreshes likewise. In position confirmation, network hubs check the situation of every hub and further guarantee that each actual area is limited by just a single character at a specific time. This paper will overview the Sybil assault recognition methods with their connected problem. At last will introduce in thoughts regarding the Sybil assault discovery through one-time restriction (just the passage into organization) with fundamentally decreases correspondence in the clouds dissimilar to different plans where per parcel area following or intermittent spread of area data is utilized to distinguish Sybil assaults. © 2020 IEEE.

关键词： Network security

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