Results for ' network analysis'

986 found
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  1.  74
    Using social network analysis as a cybernetic modelling facility for participatory design in technology-supported college curricula.Shantanu Tilak, Marvin Evans, Ziye Wen & Michael Glassman - 2023 - Systemic Practice and Action Research 36:691-724.
    Despite iterative learning design being increasingly implemented, such approaches are often delineated by well-defined periods of design/implementation. However, second-order cybernetics, which suggests a participatory approach to learning design, involves responsively adapting learning environments to meet students’ needs, treating them as agentic participants in the classroom. In our mixed methods study, we investigate whether such a process can facilitate egalitarian participation and collaborative interactions in a technology-assisted classroom. We use the example of a graduate psychology class of 17 students and suggest (...)
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  2. Examining the Network Structure among Moral Functioning Components with Network Analysis.Hyemin Han - 2024 - Personality and Individual Differences 217:112435.
    I explored the association between components constituting the basis for moral and optimal human functioning, i.e., moral reasoning, moral identity, empathy, and purpose, via network analysis. I employed factor scores instead of composite scores that most previous studies used for better accuracy in score estimation in this study. Then, I estimated the network structure among collected variables and centrality indicators. For additional information, the structure and indicators were compared between two groups, participants who engaged in civic activities (...)
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  3. Cultural evolution in Vietnam’s early 20th century: a Bayesian networks analysis of Hanoi Franco-Chinese house designs.Quan-Hoang Vuong, Quang-Khiem Bui, Viet-Phuong La, Thu-Trang Vuong, Manh-Toan Ho, Hong-Kong T. Nguyen, Hong-Ngoc Nguyen, Kien-Cuong P. Nghiem & Manh-Tung Ho - 2019 - Social Sciences and Humanities Open 1 (1):100001.
    The study of cultural evolution has taken on an increasingly interdisciplinary and diverse approach in explicating phenomena of cultural transmission and adoptions. Inspired by this computational movement, this study uses Bayesian networks analysis, combining both the frequentist and the Hamiltonian Markov chain Monte Carlo (MCMC) approach, to investigate the highly representative elements in the cultural evolution of a Vietnamese city’s architecture in the early 20th century. With a focus on the façade design of 68 old houses in Hanoi’s Old (...)
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  4. The Difficulty and Significance of Using Subjective Interpretation in Conjunction with Bayesian Network Analysis in Arts and Cultures.Minh-Hoang Nguyen & Tam-Tri Le - manuscript
    Art and Culture are very important aspects of humanity. However, due to their abstract nature, attempts to quantify the value of such fields have been the challenges for the scientific community. Recently, a new work of Vuong et al. (2019) presents an approach that sheds light on the possibility of applying Bayesian networks analysis to clarify the connection between architecture, for example, the design of the house façade and cultural evolution in Vietnamese city in the early 20th century.
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  5. Multitudinous identities: a qualitative and network analysis of the 15M collective identity.Arnau Monterde, Antonio Calleja-López, Miguel Aguilera, Xabier E. Barandiaran & John Postill - 2015 - Information, Communication and Society 18 (8):930-950.
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  6. Sentiment analysis on online social network.Vijaya Abhinandan - forthcoming - International Journal of Computer Science, Information Technology, and Security.
    A large amount of data is maintained in every Social networking sites.The total data constantly gathered on these sites make it difficult for methods like use of field agents, clipping services and ad-hoc research to maintain social media data. This paper discusses the previous research on sentiment analysis.
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  7.  48
    Advanced Network Traffic Analysis Models for Detecting Sophisticated Cyber Espionage Campaigns.V. Jain Jayant - 2025 - International Journal of Advanced Research in Cyber Security 6 (1):6-10.
    Cyber espionage campaigns pose significant challenges to global security, exploiting vulnerabilities in network infrastructures. This research paper explores advanced network traffic analysis models tailored for detecting sophisticated cyber espionage operations. The study focuses on leveraging machine learning algorithms, anomaly detection systems, and hybrid threat detection frameworks to identify subtle yet malicious activities within network traffic. Through a review of research, this paper synthesizes key findings and outlines practical applications, offering a roadmap for enhancing cybersecurity frameworks. Findings (...)
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  8. Heart attack analysis & Prediction: A Neural Network Approach with Feature Analysis.Majd N. Allouh & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (9):47-54.
    heart attack analysis & prediction dataset is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 304 samples with 11 features, such as age, sex, chest pain type, Trtbps, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, (...)
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  9.  47
    Exploring Sustainable Financial Network Analytics: Opportunities and Challenges in Systemic Risk Management and Investment Analysis.Palakurti Naga Ramesh - 2024 - International Conference on Sustainable Development Through Machine Learning, Ai and Iot 1 (1):180-188.
    This research paper delves into the realm of Financial Network Analytics, unraveling the opportunities and challenges it presents in the domains of systemic risk management and investment analysis. The study navigates the intricate web of interconnected financial entities, employing advanced analytics techniques to uncover meaningful insights. Through a comprehensive exploration, the paper identifies opportunities for leveraging financial network data to enhance systemic risk detection mechanisms and refine investment strategies. Simultaneously, it addresses the inherent challenges, such as data (...)
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  10.  56
    User Activity Analysis Via Network Traffic Using DNN and Optimized Federated Learning based Privacy Preserving Method in Mobile Wireless Networks (14th edition).Sugumar R. - 2024 - Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 14 (2):66-81.
    Mobile and wireless networking infrastructures are facing unprecedented loads due to increasing apps and services on mobiles. Hence, 5G systems have been developed to maximise mobile user experiences as they can accommodate large volumes of traffics with extractions of fine-grained data while offering flexible network resource controls. Potential solutions for managing networks and their security using network traffic are based on UAA (User Activity Analysis). DLTs (Deep Learning Techniques) have been recently used in network traffic (...) for better performances. These previously suggested techniques for network traffic analysis typically need voluminous information on network usages. Hence, this work proposes OFedeMWOUAA (optimal federated learning-based UAA technique with Meadow Wolf Optimisation) and DNN (deep Neuron Networks) for minimizing risks of data leakages in MWNs (Mobile Wireless Networks). In the proposed OFedeMWOUAA, the need to submit data to cloud servers does not arise because it trains DLTs locally and only uploads model gradients or knowledge weights. The OFedeMWOUAA approach effectively decreases dangers to data privacies with very minor performance losses in simulations. (shrink)
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  11. Alzheimer: A Neural Network Approach with Feature Analysis.Hussein Khaled Qarmout & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (10):10-18.
    Abstract Alzheimer's disease has spread insanely throughout the world. Early detection and intervention are essential to improve the chances of a positive outcome. This study presents a new method to predict a person's likelihood of developing Alzheimer's using a neural network model. The dataset includes 373 samples with 10 features, such as Group,M/F,Age,EDUC, SES,MMSE,CDR ,eTIV,nWBV,Oldpeak,ASF.. A four-layer neural network model (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 98.10% and an (...)
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  12. Predicting Audit Risk Using Neural Networks: An In-depth Analysis.Dana O. Abu-Mehsen, Mohammed S. Abu Nasser, Mohammed A. Hasaballah & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (10):48-56.
    Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, History_score, score, and Risk. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model was trained (...)
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  13. Using Network Models in Person-Centered Care in Psychiatry: How Perspectivism Could Help To Draw Boundaries.Nina de Boer, Daniel Kostić, Marcos Ross, Leon de Bruin & Gerrit Glas - 2022 - Frontiers in Psychiatry, Section Psychopathology 13 (925187).
    In this paper, we explore the conceptual problems arising when using network analysis in person- centered care (PCC) in psychiatry. Personalized network models are potentially helpful tools for PCC, but we argue that using them in psychiatric practice raises boundary problems, i.e., problems in demarcating what should and should not be included in the model, which may limit their ability to provide clinically-relevant knowledge. Models can have explanatory and representational boundaries, among others. We argue that we can (...)
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  14. Political communication in Social Networks Election campaigns and digital data analysis: a bibliographic review.Luca Corchia - 2019 - Rivista Trimestrale di Scienza Dell’Amministrazione (2):1-50.
    The outcomes of a bibliographic review on political communication, in particular electoral communication in social networks, are presented here. The electoral campaigning are a crucial test to verify the transformations of the media system and of the forms and uses of the linguistic acts by dominant actors in public sphere – candidates, parties, journalists and Gatekeepers. The aim is to reconstruct the first elements of an analytical model on the transformations of the political public sphere, with which to systematize the (...)
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  15.  57
    User Activity Analysis Via Network Traffic Using DNN and Optimized Federated Learning based Privacy Preserving Method in Mobile Wireless Networks.DrV. R. Vimal and DrR. Sugumar DrR. Udayakumar, Dr Suvarna Yogesh Pansambal, Dr Yogesh Manohar Gajmal - 2023 - INDIA: ESS- ESS PUBLICATION.
    Mobile and wireless networking infrastructures are facing unprecedented loads due to increasing apps and services on mobiles. Hence, 5G systems have been developed to maximise mobile user experiences as they can accommodate large volumes of traffics with extractions of fine-grained data while offering flexible network resource controls. Potential solutions for managing networks and their security using network traffic are based on UAA (User Activity Analysis). DLTs (Deep Learning Techniques) have been recently used in network traffic (...) for better performances. These previously suggested techniques for network traffic analysis typically need voluminous information on network usages. Hence, this work proposes OFedeMWOUAA (optimal federated learning-based UAA technique with Meadow Wolf Optimisation) and DNN (deep Neuron Networks) for minimizing risks of data leakages in MWNs (Mobile Wireless Networks). In the proposed OFedeMWOUAA, the need to submit data to cloud servers does not arise because it trains DLTs locally and only uploads model gradients or knowledge weights. The OFedeMWOUAA approach effectively decreases dangers to data privacies with very minor performance losses in simulations. (shrink)
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  16. Power of Networks and Peer Pressure: An analysis of Slum Sanitation Program in Mumbai.Vivek Anand Asokan - 2017 - International Journal Sustainable Future for Human Security 5 (2):11-20.
    With the advent of the “Clean India” campaign in India, a renewed focus on cleanliness has started, with a special focus on sanitation. There have been efforts in the past to provide sanitation related services. However, there were several challenges in provisioning. Provision of sanitation is a public health imperative given increased instances of antimicrobial resistance in India. This paper focuses on sanitation provisioning in the city of Mumbai, especially in the slums of Mumbai. The paper compares and contrasts different (...)
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  17. Predicting Kidney Stone Presence from Urine Analysis: A Neural Network Approach using JNN.Amira Jarghon & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (9):32-39.
    Kidney stones pose a significant health concern, and early detection can lead to timely intervention and improved patient outcomes. This research endeavours to predict the presence of kidney stones based on urine analysis, utilizing a neural network model. A dataset of 552 urine specimens, comprising six essential physical characteristics (specific gravity, pH, osmolarity, conductivity, urea concentration, and calcium concentration), was collected and prepared. Our proposed neural network architecture, featuring three layers (input, hidden, output), was trained and validated, (...)
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  18. Can Real Social Epistemic Networks Deliver the Wisdom of Crowds?Emily Sullivan, Max Sondag, Ignaz Rutter, Wouter Meulemans, Scott Cunningham, Bettina Speckmann & Mark Alfano - 2014 - In Tania Lombrozo, Joshua Knobe & Shaun Nichols, Oxford Studies in Experimental Philosophy, Volume 1. Oxford, GB: Oxford University Press UK.
    In this paper, we explain and showcase the promising methodology of testimonial network analysis and visualization for experimental epistemology, arguing that it can be used to gain insights and answer philosophical questions in social epistemology. Our use case is the epistemic community that discusses vaccine safety primarily in English on Twitter. In two studies, we show, using both statistical analysis and exploratory data visualization, that there is almost no neutral or ambivalent discussion of vaccine safety on Twitter. (...)
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  19. A probabilistic analysis of cross‐examination using Bayesian networks.Marcello Di Bello - 2021 - Philosophical Issues 31 (1):41-65.
    Philosophical Issues, Volume 31, Issue 1, Page 41-65, October 2021.
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  20. Neural Network-Based Water Quality Prediction.Mohammed Ashraf Al-Madhoun & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (9):25-31.
    Water quality assessment is critical for environmental sustainability and public health. This research employs neural networks to predict water quality, utilizing a dataset of 21 diverse features, including metals, chemicals, and biological indicators. With 8000 samples, our neural network model, consisting of four layers, achieved an impressive 94.22% accuracy with an average error of 0.031. Feature importance analysis revealed arsenic, perchlorate, cadmium, and others as pivotal factors in water quality prediction. This study offers a valuable contribution to enhancing (...)
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  21. Predictive Modeling of Breast Cancer Diagnosis Using Neural Networks:A Kaggle Dataset Analysis.Anas Bachir Abu Sultan & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):1-9.
    Breast cancer remains a significant health concern worldwide, necessitating the development of effective diagnostic tools. In this study, we employ a neural network-based approach to analyze the Wisconsin Breast Cancer dataset, sourced from Kaggle, comprising 570 samples and 30 features. Our proposed model features six layers (1 input, 1 hidden, 1 output), and through rigorous training and validation, we achieve a remarkable accuracy rate of 99.57% and an average error of 0.000170 as shown in the image below. Furthermore, our (...)
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  22.  46
    Averting Eavesdrop Intrusion in Industrial Wireless Sensor Networks.Arul Selvan M. - 2016 - International Journal of Innovative Research in Computer Science and Engineering (Ijircse) 2 (1):8-13.
    —Industrial networks are increasingly based on open protocols and platforms that are also employed in the IT industry and Internet background. Most of the industries use wireless networks for communicating information and data, due to high cable cost. Since, the wireless networks are insecure, it is essential to secure the critical information and data during transmission. The data that transmitted is intercepted by eavesdropper can be predicted by secrecy capacity. The secrecy capacity is the difference between channel capacity of main (...)
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  23. On Networks and Dialogues.Gabriel Furmuzachi - manuscript
    This essay inquires into the possibility of extending Randall Collins' analysis (as it is presented in The Sociology of Philosophies) of the process of innovation within intellectual networks.
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  24.  38
    The Future of Finance: Opportunities and Challenges in Financial Network Analytics for Systemic Risk Management and Investment Analysis.Palakurti Naga Ramesh - 2023 - International Journal of Interdisciplinary Finance Insights 2 (2):1-20.
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  25. Frontalparietal networks involved in categorization and item working memory.Kurt Braunlich, Javier Gomez-Lavin & Carol Seger - 2015 - NeuroImage 107:146-162.
    Categorization and memory for specific items are fundamental processes that allow us to apply knowledge to novel stimuli. This study directly compares categorization and memory using delay match to category (DMC) and delay match to sample (DMS) tasks. In DMC participants view and categorize a stimulus, maintain the category across a delay, and at the probe phase view another stimulus and indicate whether it is in the same category or not. In DMS, a standard item working memory task, participants encode (...)
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  26. Artificial Neural Network for Predicting COVID 19 Using JNN.Walaa Hasan, Mohammed S. Abu Nasser, Mohammed A. Hasaballah & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (10):41-47.
    Abstract: The emergence of the novel coronavirus (COVID-19) in 2019 has presented the world with an unprecedented global health crisis. The rapid and widespread transmission of the virus has strained healthcare systems, disrupted economies, and challenged societies. In response to this monumental challenge, the intersection of technology and healthcare has become a focal point for innovation. This research endeavors to leverage the capabilities of Artificial Neural Networks (ANNs) to develop an advanced predictive model for forecasting the spread of COVID-19. It (...)
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  27. Flower-visiting social wasps and plants interaction: Network pattern and environmental complexity.Mateus Aparecido Clemente, Denise Lange, Kleber Del-Claro, Fábio Prezoto, Nubia Ribeiro Campos & Bruno Corrêa Barbosa - 2012 - Psyche: A Journal of Entomology 2012:10.
    Network analysis as a tool for ecological interactions studies has been widely used since last decade. However, there are few studies on the factors that shape network patterns in communities. In this sense, we compared the topological properties of the interaction network between flower-visiting social wasps and plants in two distinct phytophysiognomies in a Brazilian savanna (Riparian Forest and Rocky Grassland). Results showed that the landscapes differed in species richness and composition, and also the interaction networks (...)
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  28. Semantic information and the network theory of account.Luciano Floridi - 2012 - Synthese 184 (3):431-454.
    The article addresses the problem of how semantic information can be upgraded to knowledge. The introductory section explains the technical terminology and the relevant background. Section 2 argues that, for semantic information to be upgraded to knowledge, it is necessary and sufficient to be embedded in a network of questions and answers that correctly accounts for it. Section 3 shows that an information flow network of type A fulfils such a requirement, by warranting that the erotetic deficit, characterising (...)
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  29. Classification of plant Species Using Neural Network.Muhammad Ashraf Al-Azbaki, Mohammed S. Abu Nasser, Mohammed A. Hasaballah & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (10):28-35.
    Abstract: In this study, we explore the possibility of classifying the plant species. We collected the plant species from Kaggle website. This dataset encompasses 544 samples, encompassing 136 distinct plant species. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing plant Species classification accuracy and efficiency. This research explores plant Species classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 544 entries, we develop and evaluate a neural network model. Our neural (...)
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  30. Google Stock Price Prediction Using Just Neural Network.Mohammed Mkhaimar AbuSada, Ahmed Mohammed Ulian & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (10):10-16.
    Abstract: The aim behind analyzing Google Stock Prices dataset is to get a fair idea about the relationships between the multiple attributes a day might have, such as: the opening price for each day, the volume of trading for each day. With over a hundred thousand days of trading data, there are some patterns that can help in predicting the future prices. We proposed an Artificial Neural Network (ANN) model for predicting the closing prices for future days. The prediction (...)
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  31. Unlocking Literary Insights: Predicting Book Ratings with Neural Networks.Mahmoud Harara & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (10):22-27.
    Abstract: This research delves into the utilization of Artificial Neural Networks (ANNs) as a powerful tool for predicting the overall ratings of books by leveraging a diverse set of attributes. To achieve this, we employ a comprehensive dataset sourced from Goodreads, enabling us to thoroughly examine the intricate connections between the different attributes of books and the ratings they receive from readers. In our investigation, we meticulously scrutinize how attributes such as genre, author, page count, publication year, and reader reviews (...)
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  32. Australasian Journal of Philosophy 1947–2016: a retrospective using citation and social network analyses.Martin Davies & Angelito Calma - forthcoming - Global Intellectual History.
    In anticipation of the journal’s centenary in 2027 this paper provides a citation network analysis of all available citation and publication data of the Australasian Journal of Philosophy (1923–2017). A total of 2,353 academic articles containing 21,772 references were collated and analyzed. This includes 175 articles that contained author-submitted keywords, 415 publisher-tagged keywords and 519 articles that had abstracts. Results initially focused on finding the most published authors, most cited articles and most cited authors within the journal, followed (...)
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  33. Administrative networking strategies and principals’ supervisory effectiveness in secondary schools in Cross River State, Nigeria.Esther Chijioke Madukwe, Valentine Joseph Owan & Blessing Iheoma Nwannunu - 2019 - British Journal of Education 7 (4):39-48.
    This study assessed administrative networking strategies and principals’ supervisory effectiveness in assessing teachers’ notes of lessons, teachers’ instructional delivery, students’ records, and non-academic activities in Cross River State, Nigeria. Three null hypotheses were formulated accordingly to direct the study. The study adopted a descriptive survey design. Census technique was adopted in selecting the entire population of 667 secondary school administrators in Cross River State. The instruments used for data collection were two set of questionnaires designed by the researchers including: Administrative (...)
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  34. Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map.Birgitta Dresp-Langley - 2021 - Symmetry 13:299.
    Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To (...)
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  35. Predicting Heart Disease using Neural Networks.Ahmed Muhammad Haider Al-Sharif & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (9):40-46.
    Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary classification, identifying (...)
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  36. Supervision, Mentorship and Peer Networks: How Estonian Early Career Researchers Get (or Fail to Get) Support.Jaana Eigi, Katrin Velbaum, Endla Lõhkivi, Kadri Simm & Kristin Kokkov - 2018 - RT. A Journal on Research Policy and Evaluation 6 (1):01-16.
    The paper analyses issues related to supervision and support of early career researchers in Estonian academia. We use nine focus groups interviews conducted in 2015 with representatives of social sciences in order to identify early career researchers’ needs with respect to support, frustrations they may experience, and resources they may have for addressing them. Our crucial contribution is the identification of wider support networks of peers and colleagues that may compensate, partially or even fully, for failures of official supervision. On (...)
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  37. The wisdom-of-crowds: an efficient, philosophically-validated, social epistemological network profiling toolkit.Colin Klein, Marc Cheong, Marinus Ferreira, Emily Sullivan & Mark Alfano - 2023 - In Hocine Cherifi, Rosario Nunzio Mantegna, Luis M. Rocha, Chantal Cherifi & Salvatore Miccichè, Complex Networks and Their Applications XI: Proceedings of The Eleventh International Conference on Complex Networks and Their Applications: COMPLEX NETWORKS 2022 — Volume 1. Springer.
    The epistemic position of an agent often depends on their position in a larger network of other agents who provide them with information. In general, agents are better off if they have diverse and independent sources. Sullivan et al. [19] developed a method for quantitatively characterizing the epistemic position of individuals in a network that takes into account both diversity and independence; and presented a proof-of-concept, closed-source implementation on a small graph derived from Twitter data [19]. This paper (...)
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  38.  70
    Securing IoT Networks: Machine Learning-Based Malware Detection and Adaption.G. Ganesh - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (5):1-16.
    Although Internet of Things (IoT) devices are being rapidly embraced worldwide, there are still several security concerns. Due to their limited resources, they are susceptible to malware assaults such as Gafgyt and Mirai, which have the ability to interrupt networks and infect devices. This work looks into methods based on machine learning to identify and categorize malware in IoT network activity. A dataset comprising both malware and benign traffic is used to assess different classification techniques, such as Random Forest, (...)
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  39. The Politicization of the German Ethical Consumer: A Qualitative Analysis of an Ethical Fashion Network and its Production of Ethical Consumer Subjectivity.Aneka Brunßen - 2020 - Dissertation, Universität Bremen
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  40. Forecasting Stock Prices using Artificial Neural Network.Ahmed Munther Abdel Hadi & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (10):42-50.
    Abstract: Accurate stock price prediction is essential for informed investment decisions and financial planning. In this research, we introduce an innovative approach to forecast stock prices using an Artificial Neural Network (ANN). Our dataset, consisting of 5582 samples and 6 features, including historical price data and technical indicators, was sourced from Yahoo Finance. The proposed ANN model, composed of four layers (1 input, 1 hidden, 1 output), underwent rigorous training and validation, yielding remarkable results with an accuracy of 99.84% (...)
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  41.  47
    Adaptive Depth-Based Routing Protocol for Energy-Constrained Underwater Wireless Networks.M. Sheik Dawood - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):635-640.
    Underwater Wireless Sensor Networks (UWSNs) have emerged as a critical tool for various marine applications, such as underwater surveillance, oceanographic data collection, pollution monitoring, and resource exploration. These networks consist of a group of sensor nodes deployed underwater to monitor and collect data, which are then transmitted to a surface station for analysis.
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  42. Academics’ Epistemological Attitudes towards Academic Social Networks and Social Media.Jevgenija Sivoronova, Aleksejs Vorobjovs & Vitālijs Raščevskis - 2024 - Philosophies 9 (1):1-28.
    Academic social networks and social media have revolutionised the way individuals gather information and express themselves, particularly in academia, science, and research. Through the lens of academics, this study aims to investigate the epistemological and psychosocial aspects of these knowledge sources. The epistemological attitude model presented a framework to delve into and reflect upon the existence of knowledge sources, comprising subjective, interactional, and knowledge dimensions. One hundred and twenty-six university academics participated in this study, including lecturers and researchers from different (...)
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  43. Hypertext Configurations: Genres in Networked Digital Media.Niels Ole Finnemann - 2017 - Journal of the Association for Information Science and Technology 68 (4):845-854.
    The article presents a conceptual framework for distinguishing different sorts of heterogeneous digital materials. The hypothesis is that a wide range of heterogeneous data resources can be characterized and classified due to their particular configurations of hypertext features such as scripts, links, interactive processes, and time scalings, and that the hypertext configuration is a major but not sole source of the messiness of big data. The notion of hypertext will be revalidated, placed at the center of the interpretation of networked (...)
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  44. A Bias Network Approach (BNA) to Encourage Ethical Reflection Among AI Developers.Gabriela Arriagada-Bruneau, Claudia López & Alexandra Davidoff - 2025 - Science and Engineering Ethics 31 (1):1-29.
    We introduce the Bias Network Approach (BNA) as a sociotechnical method for AI developers to identify, map, and relate biases across the AI development process. This approach addresses the limitations of what we call the "isolationist approach to AI bias," a trend in AI literature where biases are seen as separate occurrence linked to specific stages in an AI pipeline. Dealing with these multiple biases can trigger a sense of excessive overload in managing each potential bias individually or promote (...)
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  45. Explaining the behaviour of random ecological networks: the stability of the microbiome as a case of integrative pluralism.Roger Deulofeu, Javier Suárez & Alberto Pérez-Cervera - 2019 - Synthese 198 (3):2003-2025.
    Explaining the behaviour of ecosystems is one of the key challenges for the biological sciences. Since 2000, new-mechanicism has been the main model to account for the nature of scientific explanation in biology. The universality of the new-mechanist view in biology has been however put into question due to the existence of explanations that account for some biological phenomena in terms of their mathematical properties (mathematical explanations). Supporters of mathematical explanation have argued that the explanation of the behaviour of ecosystems (...)
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  46. Conceptual Analysis in Metaethics.N. G. Laskowski & Stephen Finlay - 2017 - In Tristram Colin McPherson & David Plunkett, The Routledge Handbook of Metaethics. New York: Routledge. pp. 536-551.
    A critical survey of various positions on the nature, use, possession, and analysis of normative concepts. We frame our treatment around G.E. Moore’s Open Question Argument, and the ways metaethicists have responded by departing from a Classical Theory of concepts. In addition to the Classical Theory, we discuss synthetic naturalism, noncognitivism (expressivist and inferentialist), prototype theory, network theory, and empirical linguistic approaches. Although written for a general philosophical audience, we attempt to provide a new perspective and highlight some (...)
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  47. Books’ Rating Prediction Using Just Neural Network.Alaa Mazen Maghari, Iman Ali Al-Najjar, Said Jamil Al-Laqtah & Samy S. Abu-Naser - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 4 (10):17-22.
    Abstract: The aim behind analyzing the Goodreads dataset is to get a fair idea about the relationships between the multiple attributes a book might have, such as: the aggregate rating of each book, the trend of the authors over the years and books with numerous languages. With over a hundred thousand ratings, there are books which just tend to become popular as each day seems to pass. We proposed an Artificial Neural Network (ANN) model for predicting the overall rating (...)
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  48. The Meaning-Sharing Network.John Haglund & Johan Blomberg - 2010 - Hortues Semioticus 6:17-30.
    We advocate an analysis of meaning that departs from the pragmatic slogan that “meaning is use”. However, in order to avoid common missteps, this claim is in dire need of qualification. We argue that linguistic meaning does not originate from language use as such; therefore we cannot base a theory of meaning only on use. It is important not to neglect the fact that language is ultimately reliant on non-linguistic factors. This might seem to oppose the aforementioned slogan, but (...)
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  49. Leveraging Artificial Neural Networks for Cancer Prediction: A Synthetic Dataset Approach.Mohammed S. Abu Nasser & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (11):43-51.
    Abstract: This research explores the application of artificial neural networks (ANNs) in predicting cancer using a synthetically generated dataset designed for research purposes. The dataset comprises 10,000 pseudo-patient records, each characterized by gender, age, smoking history, fatigue, and allergy status, along with a binary indicator for the presence or absence of cancer. The 'Gender,' 'Smoking,' 'Fatigue,' and 'Allergy' attributes are binary, while 'Age' spans a range from 18 to 100 years. The study employs a three-layer ANN architecture to develop a (...)
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  50. Analysis of Cyber Security In E-Governance Utilizing Blockchain Performance.Regonda Nagaraju, Selvanayaki Shanmugam, Sivaram Rajeyyagari, Jupeth Pentang, B. Kiran Bala, Arjun Subburaj & M. Z. M. Nomani - manuscript
    E-Government refers to the administration of Information and Communication Technologies (ICT) to the procedures and functions of the government with the objective of enhancing the transparency, efficiency and participation of the citizens. E-Government is tough systems that require distribution, protection of privacy and security and collapse of these could result in social and economic costs on a large scale. Many of the available e-government systems like electronic identity system of management (eIDs), websites are established at duplicated databases and servers. An (...)
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