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 (...) highly versus lowly. The results demonstrated significant intra- and inter-scale associations in the network. The network structure was invariant across the two groups. Despite the network invariance, I found that the global connectivity between and centrality indicators of examined factors were higher among the high civic engagement group in general. I discussed the implications of the findings in research on moral functioning based on moral psychology and virtue ethics. (shrink)

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 (...) that adaptation of live-chat activities by a participant observer on the Reddit social media platform that supplemented the in-person lecture dynamically, using a network analysis and qualitative ethnography as a modelling facility mimicked the ongoing feedback loops of social media platforms, enabling students to use social media with a critical eye, and engage in productive collaboration. Our quantitative results present network graphs for weekly eigen centrality to understand the egalitarian nature of the network, and transitivity to understand the likelihood for collaboration between more than two agents. Our qualitative results elaborate selected Reddit posts, and weekly field notes to explain how redesigning the chat weekly helped augment lecture-based discussion with the instructor and critique of student presentations, spurring egalitarian participation through a space-place dialectic. Students also provided end-semester feedback that was analyzed using inductive coding, to design future courseware. (shrink)

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 (...) Quarter (based on 78 data lines extracted from 248 photos), the study argues that it is plausible to look at the aesthetics, architecture, and designs of the house façade to find traces of cultural evolution in Vietnam, which went through more than six decades of French colonization and centuries of sociocultural influence from China. The in-depth technical analysis, though refuting the presumed model on the probabilistic dependency among the variables, yields several results, the most notable of which is the strong influence of Buddhism over the decorations of the house façade. Particularly, in the top 5 networks with the best Bayesian Information Criterion (BIC) scores and p<0.05, the variable for decorations (DC) always has a direct probabilistic dependency on the variable B for Buddhism. The paper then checks the robustness of these models using Hamiltonian MCMC method and find the posterior distributions of the models’ coefficients all satisfy the technical requirement. Finally, this study suggests integrating Bayesian statistics in the social sciences in general and for the study of cultural evolution and architectural transformation in particular. (shrink)

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.

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, (...) and HeartDisease. A neural network model with four layers (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 88% and an average error of 0078296. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

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 (...) class='Hi'>analysis 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)

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 (...) highlight the efficacy of deep learning and unsupervised models in mitigating espionage-related risks, paving the way for more robust detection systems. (shrink)

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 (...) complexity and dynamic network structures, offering nuanced perspectives on their implications. The findings contribute to the evolving landscape of financial analytics, providing a foundation for the development of robust tools and methodologies that can aid financial institutions and investors in navigating the complexities of modern financial networks. (shrink)

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.

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 (...) class='Hi'>analysis 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)

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 (...) average error of 0.01. This study provides a valuable tool for early detection and intervention of Alzheimer's, thus contributing to the field of health and medicine. (shrink)

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 (...) make more explicit what kind of questions personalized network models can address in PCC, given their representational and explanatory boundaries, using perspectival reasoning. (shrink)

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 (...) results of the main empirical research carried out in recent years, in particular those conducted with a promising methodology: Digital Trace Data Analysis. (shrink)

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 (...) and validated, achieving an impressive accuracy of 100% and an average error of 0.000015, indicating its robust predictive capability. Moreover, we conducted feature importance analysis to identify the most influential features for predicting audit risk. The key features found to be critical for classifying fraudulent activities in audit risk prediction are Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, and History_score. This research contributes to the field of audit risk prediction by demonstrating the effectiveness of a neural network-based approach and highlighting the importance of specific features in detecting fraudulent activities. The findings have significant implications for auditors and organizations seeking to enhance their audit risk assessment processes, ultimately leading to improved financial transparency and fraud detection. (shrink)

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. (...) Roughly half the accounts engaging with this topic are pro-vaccine, while the other half are con-vaccine. We also show that these two camps rarely engage with one another, and that the con-vaccine camp has greater epistemic reach and receptivity than the pro-vaccine camp. In light of these findings, we question whether testimonial networks as they are currently constituted on popular fora such as Twitter are living up to their promise of delivering the wisdom of crowds. We conclude by pointing to directions for further research in digital social epistemology. (shrink)

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, (...) achieving an impressive accuracy of 98.67% and an average error of 0.012. In addition to model performance, feature importance analysis was conducted to determine the most influential factors in predicting kidney stone presence. The findings underscore the significance of urea concentration, specific gravity, calcium concentration, conductivity, osmolarity, and pH as key indicators. This research contributes to the early diagnosis of kidney stones and demonstrates the potential of neural networks in medical diagnostics. The clinical implications of these findings are discussed, emphasizing the importance of timely intervention in managing kidney stone-related health issues. (shrink)

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 (...) models of sanitation provision including supply-led provisioning of sanitation by the Indian government to demand-led provisioning of sanitation through a World Bank funded “Slum Sanitation Program” (SSP). The paper also outlines a comparative perspective on the implementation and usage of toilet blocks. The author presents the theory of social networks and positive peer pressure and argues that these will amplify the effect of other incentives. With the help of an illustration, this paper concludes that the sustainable sanitation policy should look at facilitating and creating the infrastructure as a network and not strictly at building toilet blocks. (shrink)

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 (...) water quality monitoring and decision-making for stakeholders and policymakers. (shrink)

Philosophical Issues, Volume 31, Issue 1, Page 41-65, October 2021.

—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 (...) link and wiretap link of wireless transmission. When the secrecy capacity gets fades, then it is known that transmitted data is intercepted. In the event, of applying optimal sensor scheduling scheme a sensor with highest secrecy capacity is chosen and data is transmitted. It is followed with cooperative localization algorithm to enhance nodes, predict and maintain power of each sensor nodes. Through localization algorithm best sensor nodes are get predicted for transmission. Moreover, RC4 encryption and decryption algorithm is enhanced for data while transmission. This will enable protection to data to an extent. Also, an asymptotic intercept probability analysis is performed to provide an imminent into the impact of the sensor scheduling on the wireless security. (shrink)

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 (...) investigation identifies the most influential features in breast cancer diagnosis, shedding light on the key determinants of malignancy. Notably, we find that factors such as fractal dimension_se, symmetry worst, compactness_worst, symmetry_se, and smoothness_se play pivotal roles in distinguishing between benign and malignant cases. This research contributes to the ongoing efforts to enhance breast cancer diagnosis, providing valuable insights into feature importance and showcasing the potential of neural networks in medical applications. Our findings have implications for improving early detection and treatment strategies, ultimately contributing to improved patient outcomes. (shrink)

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 (...) influence a book's overall rating. Through rigorous analysis and experimentation, we construct an advanced ANN model tailored for predictive analysis in the realm of book ratings. The outcomes of our study reveal the remarkable potential of ANNs in this domain. The ANN model exhibits an impressive level of accuracy when it comes to forecasting book ratings, underlining the efficacy and promise of artificial neural networks in enhancing our understanding and prediction of book evaluations. This research opens up new avenues for leveraging machine learning techniques to gain deeper insights into the dynamics of book ratings and reader preferences. (shrink)

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.

Electrocardiogram (ECG) signal analysis is a critical task in healthcare for diagnosing cardiovascular conditions such as arrhythmias, heart attacks, and other heart-related diseases. With the growth of Internet of Things (IoT) networks, real-time ECG monitoring has become possible through wearable devices and sensors, providing continuous patient health monitoring. However, real-time ECG signal analysis in IoT environments poses several challenges, including data latency, limited computational power of IoT devices, and energy constraints. This paper proposes a framework for Optimized Machine (...) Learning Algorithms designed to analyze ECG signals in real time within IoT networks. The proposed system leverages lightweight machine learning models, including support vector machines (SVM) and convolutional neural networks (CNNs), optimized to run efficiently on low-power IoT devices while maintaining high accuracy. The system addresses the computational limitations of IoT devices by employing edge computing techniques that distribute the processing load between IoT devices and edge servers. Additionally, data compression and feature extraction techniques are applied to reduce the size of the data transmitted over the network, thereby minimizing latency and bandwidth usage. This paper reviews the current advancements in real-time ECG analysis, explores the challenges posed by IoT environments, and presents the optimized machine learning algorithms that enhance real-time monitoring of heart health. The system is evaluated for its performance in terms of accuracy, energy efficiency, and data transmission speed, showing promising results in improving real-time ECG signal analysis in resource-constrained IoT networks. (shrink)

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 (...) and maintain a specific individual item, and at probe decide if the stimulus is an exact match or not. Constrained Principal Components Analysis was used to identify and compare activity within neural networks associated with these tasks, and we relate these networks to those that have been identified with resting state-fMRI. We found that two frontoparietal networks of particular interest. The first network included regions associated with the dorsal attention network and frontoparietal salience network; this network showed patterns of activity consistent with a role in rapid orienting to and processing of complex stimuli. The second uniquely involved regions of the frontoparietal central executive network; this network responded more slowly following each stimulus and showed a pattern of activity consistent with a general role in role in decision-making across tasks. Additional components were identified that were associated with visual, somatomotor and default mode networks. (shrink)

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 (...) Networking Strategies Questionnaire (ANSQ)”, and “Principals’ Supervisory Effectiveness Questionnaire (PSEQ)” respectively. The reliability of the instruments was established through Cronbach Alpha, and reliability estimates of .89 and .92 were obtained for the two instruments respectively. The null hypotheses were all tested at .05 level of significance using Pearson Product Moment Correlation Analysis with the aid of SPSS v21. Findings from the study revealed that; there is a significant relationship between principals’ effective communication, school-community relationship, and teachers’ involvement in decision-making, with their supervisory effectiveness respectively. Based on the findings of the study, it was recommended amongst others that; secondary school principals should endeavour to communicate relevant ideas, messages, and information to both teaching and non-academic staff of the school; and ensure that appropriate feedback mechanisms are provided based on such information. (shrink)

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 (...) this end, we exploit a neural network metric in the output of a biologically inspired Self- Organizing Map Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, a longer choice response time (RT) for “yes” responses relative to symmetry. These data are consistently mirrored by the variations in the SOM-QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metric’s proven selectivity to local contrast and color variations in large and highly complex image data. (shrink)

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 (...) between wasps and plants had different patterns. The network was more complex in the Riparian Forest, with a larger number of species and individuals and a greater amount of connections between them. The network specialization degree was more generalist in the Riparian Forest than in the Rocky Grassland. This result was corroborated by means of the nestedness index. In both networks was found asymmetry, with a large number of wasps per plant species. In general aspects, most wasps had low niche amplitude, visiting from one to three plant species. Our results suggest that differences in structural complexity of the environment directly influence the structure of the interaction network between flower-visiting social wasps and plants. (shrink)

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 (...) the target semantic information t by default, is correctly satisfied by the information flow of correct answers provided by an informational source s. Section 4 illustrates some of the major advantages of such a Network Theory of Account (NTA) and clears the ground of a few potential difficulties. Section 5 clarifies why NTA and an informational analysis of knowledge, according to which knowledge is accounted semantic information, is not subject to Gettier-type counterexamples. A concluding section briefly summarises the results obtained. (shrink)

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 (...) the basis of our analysis we argue that support for early career researchers should take into account the resources they already possess but also recognise the importance of wider academic culture, including funding and employment patterns, and the roles of supervisors and senior researchers in ensuring successful functioning of support networks. Through analysing the conditions for the development of early career researchers – producers of knowledge – our paper contributes to social epistemology understood as analysis of specific forms of social organisation of knowledge production. (shrink)

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 (...) by most discussed topics and emerging patterns using keywords and abstracts. The analysis then proceeded to apply social network analysis using Kumu© – a visualization platform for mapping systems and relationships using large datasets. Analysis reveals topic clusters both unique to the journal, and inclusive of the journal’s history. Results from this analysis reaffirm the journal’s continuing focus on topics in traditional analytic philosophy such as morality, epistemology and knowledge, whilst also featuring topics associated with logic and paradox. This paper presents a new approach to analysing and understanding the historic and emerging topics of interest to the journal, and its readership. This has never previously been done for single philosophy journal. This is historically important given the journal’s forthcoming centenary. (shrink)

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 (...) involves the collection, analysis, and integration of diverse datasets encompassing epidemiological, clinical, and social factors that influence the virus's dissemination. (shrink)

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 (...) is based on these features (date, opening price, highest price, lowest price, volume), which were used as input variables and (closing price) as output variable for our ANN model. Our model was created, trained, and validated using data set in JNN environment, which its title is “Google-Stock-Prices”. The model evaluation showed that the ANN model is able to predict correctly 99.48% of the validation samples. (shrink)

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 (...) reports on an open-source reimplementation of their algorithm in Python, optimized to be usable on much larger networks. In addition to the algorithm and package, we also show the ability to scale up our package to large synthetic social network graph profiling, and finally demonstrate its utility in analyzing real-world empirical evidence of ‘echo chambers’ on online social media, as well as evidence of interdisciplinary diversity in an academic communications network. (shrink)

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 (...) the adoption of an uncritical approach to understanding the influence of biases in developers’ decision-making. The BNA fosters dialogue and a critical stance among developers, guided by external experts, using graphical representations to depict biased connections. To test the BNA, we conducted a pilot case study on the "waiting list” project, involving a small AI developer team creating a healthcare waiting list NPL model in Chile. The analysis showed promising findings: (i) the BNA aids in visualizing interconnected biases and their impacts, facilitating ethical reflection in a more accessible way; (ii) it promotes transparency in decision-making throughout AI development; and (iii) more focus is necessary on professional biases and material limitations as sources of bias in AI development. (shrink)

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 (...) the presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

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 (...) of books. The prediction is based on these features (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which were used as input variables and (average_rating) as output variable for our ANN model. Our model were created, trained, and validated using data set in JNN environment, which its title is “Goodreads-books”. Model evaluation showed that the ANN model is able to predict correctly 99.78% of the validation samples. (shrink)

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, (...) XGBoost, Logistic Regression, etc., for multi-class malware detection. After a great deal of empirical testing, XGBoost comes out on top, providing 99.9% recall and accuracy. Both known and unknown malware can be detected by the trained model with remarkable precision. The use of transfer learning, where the XGBoost model is used as a basis for the rapid construction of new malware detection models, is one of the major innovations put forth. This makes it possible to quickly adjust to new dangers. More information about how real-time network traffic can be monitored with the help of the created model to find irregularities and sound the alarm. An intelligent and proactive security solution for IoT environments is offered by the machine learning technique that is being discussed. This is an efficient defense against malware because of its high accuracy, low false positive rate, real-time detection capability, and adaptability to new malware varieties changing risks associated with IoT. The suggested methods will assist in securing susceptible IoT devices and networks from obstructive malware assaults. (shrink)

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 (...) digital media, and used in the analysis of the fast-growing amounts of heterogeneous digital collections, assemblages, and corpora. The introduction summarizes the wider background of a fast-changing data landscape. (shrink)

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 of (...) books. The prediction is based on these features (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which were used as input variables and (average_rating) as output variable for our ANN model. Our model were created, trained, and validated using data set in JNN environment, which its title is “Goodreads-books”. Model evaluation showed that the ANN model is able to predict correctly 99.78% of the validation samples. (shrink)

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 (...) underappreciated problems about normative concepts. (shrink)

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 (...) is usually provided in terms of their mathematical properties, and not in mechanistic terms. They have intensively studied the explanation of the properties of ecosystems that behave following the rules of a non-random network. However, no attention has been devoted to the study of the nature of the explanation in those that form a random network. In this paper, we cover that gap by analysing the explanation of the stability behaviour of the microbiome recently elaborated by Coyte and colleagues, to determine whether it fits with the model of explanation suggested by the new-mechanist or by the defenders of mathematical explanation. Our analysis of this case study supports three theses: (1) that the explanation is not given solely in terms of mechanisms, as the new-mechanists understand the concept; (2) that the mathematical properties that describe the system play an essential explanatory role, but they do not exhaust the explanation; (3) that a non-previously identified appeal to the type of interactions that the entities in the network can exhibit, as well as their abundance, is also necessary for Coyte and colleagues’ account to be fully explanatory. From the combination of these three theses we argue for the necessity of an integrative pluralist view of the nature of behaviour explanation when this is given by appealing to the existence of a random network. (shrink)

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.

Introduction. The development of online marketing in social networks creates unique opportunities for personal selling. Especially these opportunities are manifested in online education when they buy a brand of an expert with experience in a particular field. That is why a competitive space is being formed in the Instagram social network, where a personal brand acts as a product or service. -/- Materials and methods. Studying the effectiveness of promoting a personal brand in social networks based on the Instagram (...) platform was chosen to have great visual opportunities for self-presentation. As part of the collection of empirical material, two methods were used: a survey (N-200) and content analysis of three blogger accounts with high rates of activity and popularity. -/- Results and discussion. Content analysis of bloggers showed that an algorithmic feed on a social network allows bloggers to control the content. To help them, Instagram provides statistical data on user reach, thereby capturing trends in the movement of the blogger’s audience. The main task of a blogger is to combine real and “virtual” images so as not to lose consumer confidence. A survey of social network users confirmed the importance of a personal brand for them. The survey also made it possible to identify the most popular audience requests that they expect from bloggers and their accounts: valuable reviews and recommendations, case studies and author’s solutions, storytelling, blogger’s reflections and motivating messages, live broadcasts, and stories are important. -/- Conclusions. The results obtained underline followers’ high level of interest in the bloggers’ personal brand on Instagram. The study results show that the most significant number of those involved (those who give feedback and are constantly involved in interaction with the account) is where the formation of a personal brand is built to a greater extent on the emotional level of perception of the individual. (shrink)

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 (...) network, featuring a single hidden layer, achieves remarkable results—a staggering 100% accuracy and a minute average error rate of 0.002. Beyond performance metrics, we delve into the intricacies of plant Species classification through feature importance analysis. The most influential features— Vegsout, durflow, semiros, pdias, begflow, wind, leafy, autopoll and insects— uncover the physiological traits underpinning accurate rice classification. This research contributes to advancing rice classification methods and highlights the potential of ANNs in optimizing agricultural practices, ensuring plant safety, and bolstering global trade. (shrink)

Many Public Administrations structure their services around the notion of users’ need. However, there is a gap between private, subjectively perceived needs (self-attributed) and needs that are attributed by PA to citizens (heteroattributed). Because of the gap, citizens’ needs are often only partially satisfied by PAs services. This gap is in part due to the fact that the meaning of the word “need” is ambiguous and full of antinomic nuances. The purpose of this paper is to formulate a definition of (...) “need” suitable for citizens’ needs management with respect to PA’s services offering, and to provide an accurate ontological analysis of the notion of “need” and the network of concepts that relate to it. (shrink)

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 (...) higher education institutions in Latvia. The study employed two methods: the Epistemological Attitudes towards Sources of Knowledge Questionnaire and the Epistemological Attitudes towards Sources of Knowledge Semantic Questionnaire. The data analysis involved several procedures, including exploratory and confirmatory factor analysis, correlation analysis, and test statistics. By implementing these methods, the study gained valuable insights into the sources of knowledge, examining them from two perspectives. The first perspective brought attention to the differences in academics’ appraisals by discussing their understanding, approach, use, and valuations of these sources. By scrutinising the constructs of meanings, the second perspective sheds light on the anticipated knowledge which is deemed ideal, the concrete knowledge that is both social and objective, and the subjectively valuable nature of academic social networks and social media. The findings underscore the specialised knowledge and qualities that academics rely on for producing knowledge. In terms of epistemology, methodology, social science, and education, the study holds theoretical and practical implications, especially in comprehending knowledge and its sources. (shrink)