Abstract: Early detection of breast cancer plays a pivotal role in improving patient prognosis and reducing mortality rates. Recent technological advancements have significantly enhanced the accuracy and effectiveness of breast cancer screening methods. This paper explores the latest innovations in early detection, including the evolution of digital mammography, the impact of 3D mammography (tomosynthesis), and the use of advanced imaging techniques such as molecular imaging and MRI. Furthermore, the integration of artificial intelligence (AI) in diagnostic tools (...) is discussed, highlighting how machine learning algorithms are refining imaging analyses and reducing diagnostic errors. Advances in genetic screening, liquid biopsies, and biomarkers are also examined, showcasing their potential in identifying high-risk individuals and enabling personalized treatment plans. This paper provides insights into the future of breast cancer detection, outlining both the opportunities and challenges that lie ahead in adopting these innovative technologies to improve patient outcomes. (shrink)

Abstract: Early detection of breast cancer is vital for improving patient outcomes and reducing mortality rates. Technological advancements have significantly enhanced the accuracy and efficiency of screening methods. This paper explores recent innovations in early detection, focusing on the evolution of digital mammography, the benefits of 3D mammography (tomosynthesis), and the application of advanced imaging techniques such as molecular imaging and MRI. It also examines the role of artificial intelligence (AI) in diagnostic tools, showing how machine (...) learning algorithms are improving imaging analysis and reducing diagnostic errors. Furthermore, the paper discusses advancements in genetic screening, liquid biopsies, and biomarkers, emphasizing their potential in identifying high-risk individuals and supporting personalized treatment plans. The paper provides a forward-looking perspective on the future of breast cancer detection, addressing both the opportunities and challenges associated with the adoption of these cutting-edge technologies to improve patient outcomes. (shrink)

Community detection is a major issue in network analysis. This paper combines a socio-historical approach with an experimental reconstruction of programs to investigate the early automation of clique detection algorithms, which remains one of the unsolved NP-complete problems today. The research led by the archaeologist Jean-Claude Gardin from the 1950s on non-numerical information and graph analysis is retraced to demonstrate the early contributions of social sciences and humanities. The limited recognition and reception of Gardin's innovative computer (...) application to the humanities are addressed through two factors, in addition to the effects of historiography and bibliographies on the recording, discoverability, and reuse of scientific productions: (1) funding policies, evidenced by the transfer of research effort on graph applications from temporary interdisciplinary spaces to disciplinary organizations related to the then-emerging field of computer science; and (2) the erratic careers of algorithms, in which efficiency, flaws, corrections, and authors’ status, were determining factors. (shrink)

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and reacts like humans, some of the computer activities with artificial intelligence are designed to include speech, recognition, learning, planning and problem solving. Deep learning is a collection of algorithms used in machine learning, it is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is used as a (...) technique to produce brain tumor detection and classification models using Magnetic Resonance Imaging (MRI) imaging for rapid and easy detection and identification of brain tumor. In this thesis, some ways and mechanisms will be reviewed to use deep learning techniques to produce a model for brain tumor detection. The goal is to find a good and effective way to detect brain tumor based on MRI to help the brain doctor in making decisions easily, accurately and rapidly. A recent report by the World Health Organization in February 2018 showed that the death rate from brain cancer or central nervous system (CNS) is the highest in the Asian continent. It is important to detect cancer early so that many of these lives can be saved. The model has been designed and implemented, including a dataset which consist of 10,000 images for brain tumor detection through the use of Deep learning algorithms based on neural networks. For testing, we have used our model, Inception, VGG16, MobileNet and ResNet models. The f-score accuracy we got for each model was as follows: Our model was 98.28, VGG16 was 99.86%, ResNet50 was 98.14%, MobileNet was 88,98%, and InceptionV3 was 99.88%. (shrink)

The health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases (...) with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. A. (shrink)

he health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases (...) with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. (shrink)

We show that early vision can use monocular cues to rapidly complete partially-occluded objects. Visual search for easily detected fragments becomes difficult when the completed shape is similar to others in the display; conversely, search for fragments that are difficult to detect becomes easy when the completed shape is distinctive. Results indicate that completion occurs via the occlusion-triggered removal of occlusion edges and linking of associated regions. We fail to find evidence for a visible filling-in of contours or surfaces, (...) but do find evidence for a "functional" filling-in that prevents the constituent fragments from being rapidly accessed. As such, it is only the completed structures—and not the fragments themselves—that serve as the basis for rapid recognition. (shrink)

This study aimed to evaluate if eye blinks can be used to discriminate players with different performance in a session of Nintendo Entertainment System (NES) Tetris. To that end, we developed a state-of-the-art method for blink extraction from EAR measures, which is robust enough to be used with data collected by a low-grade webcam such as the ones widely available on laptop computers. Our results show a significant decrease in blink rate per minute (blinks/m) during the first minute of playing (...) Tetris. After having defined 3 groups of proficiency based on in-game performance (Novices, Intermediates, and Experts) we found out that expert players display a significantly lower decrease in blinks/m compared to novices during the first minute of gameplay, which shows that Tetris players' proficiency can be detected by looking at eye blinks/m variations during the early phase of a game session. This difference in blinks/m is observed throughout the entire game session, which supports the general conclusion that proficient Tetris players have a lower decrease in blinks/m, even when playing more difficult levels. Finally, we offer some interpretations of this effect and the relationship that our results may have with the visual cognitive workload experienced during the gameplay. (shrink)

Western philosophy begins with Thales, Anaximander, and Anaximenes. Or so we are told by Aristotle and many members of the later doxographical tradition. But a good case can be made that several centuries before the Milesian thinkers began their investigations, the poets of archaic Greece reflected on the limits of human intelligence and concluded that no mortal being could know the full and certain truth. Homer belittled the mental capacities of ‘creatures of a day’ and a series of poets of (...) the archaic period declared that mortals think only in terms of ‘what they meet with’ while failing to detect the larger cycles that make for success or failure. While Xenophanes embraced this pessimistic tradition, a number of early thinkers (Heraclitus, Parmenides, Empedocles, and Philolaus) expressed a more positive view of the powers of human intelligence, at least in those who heard and took to heart their novel philosophical teachings. (shrink)

To secure computers and information systems from attackers taking advantage of vulnerabilities in the system to commit cybercrime, several methods have been proposed for real-time detection of vulnerabilities to improve security around information systems. Of all the proposed methods, machine learning had been the most effective method in securing a system with capabilities ranging from early detection of software vulnerabilities to real-time detection of ongoing compromise in a system. As there are different types of cyberattacks, each (...) of the existing state-of-the-art machine learning models depends on different algorithms for training which also impact their suitability for detection of a particular type of cyberattack. In this research, we analyzed each of the current state-of-the art machine learning models for different types of cyberattack detection from the past 10 years with a major emphasis on the most recent works for comparative study to identify the knowledge gap where work is still needed to be done with regard to detection of each category of cyberattack. (shrink)

The mental risk poses a high threat to the individuals, especially overseas demographic, including expatriates in comparison to the general Arab demographic. Since Arab countries are renowned for their multicultural environment with half of the population of students and faculties being international, this paper focuses on a comprehensive analysis of mental health problems such as depression, stress, anxiety, isolation, and other unfortunate conditions. The dataset is developed from a web-based survey. The detailed exploratory data analysis is conducted on the dataset (...) collected from Arab countries to study an individual’s mental health and indicative help-seeking pointers based on their responses to specific pre-defined questions in a multicultural society. The proposed model validates the claims mathematically and uses different machine learning classifiers to identify individuals who are either currently or previously diagnosed with depression or demonstrate unintentional “save our souls” (SOS) behaviors for an early prediction to prevent risks of danger in life going forward. The accuracy is measured by comparing with the classifiers using several visualization tools. This analysis provides the claims and authentic sources for further research in the multicultural public medical sector and decision-making rules by the government. (shrink)

When brief blank fields are placed between alternating displays of an original and a modified scene, a striking failure of perception is induced: the changes become extremely difficult to notice, even when they are large, presented repeatedly, and the observer expects them to occur (Rensink, O'Regan, & Clark, 1997). To determine the mechanisms behind this induced "change blindness", four experiments examine its dependence on initial preview and on the nature of the interruptions used. Results support the proposal that representations at (...) the early stages of visual processing are highly volatile, and that focused attention is needed to stabilize them sufficiently to support the perception of change. (shrink)

To say Early Confucians advocated the possession of a sense of shame as a means to moral virtue underestimates the tact and forethought they used successfully to mold natural dispositions to experience shame into a system of self, familial, and social governance. Shame represents an adaptive system of emotion, cognition, perception, and behavior in social primates for measurement of social rank. Early Confucians understood the utility of the shame system for promotion of cooperation, and they build and deploy (...) cultural modules – e.g., rituals, titles, punishments – with this in mind. These policies result in subtle alterations to components of the evolved shame system that are detectable in data form contemporary cross-cultural psychology that show that populations in the Confucian diaspora have a unique shame profile compared to Western and non-Western populations. The status of Confucian diaspora populations as outliers in the context of shame is partially explained by appeal to the cultural transmission and historical endurance of relevant Early Confucian cultural modules. (shrink)

Taiwan and Vietnam have taken successful measures to combat the spread of COVID-19 at the early stages. Many authors attributed the successful policies to the lessons learned by these countries during the severe acute respiratory syndrome (SARS) pandemic in 2002.(Ohara, 2004) This manuscript provides a summary of recent early-stage policies that were successful in mitigating the spread and creating resilience against the negative consequences of COVID-19 in Taiwan and Vietnam. Crucially, these policies go beyond and complement social isolation. (...) As social isolation is expected to have a negative socio-economic impact on the population and adherence is likely to decrease with time(Armitage and Nellums, 2020; Weems et al., 2020), it is important to consider a broad range of policies to promote a steady control of the COVID-19 spread. Initially, we provide a brief introduction to some general concepts related to COVID-19. Thereafter, we introduce a concise review of policies and their dates relative to the first detection case in Taiwan and Vietnam as well as doing a comparative analysis. (shrink)

Mindfulness training has been used to improve emotional wellbeing in early adolescents. However, little is known about treatment outcome moderators, or individual differences that may differentially impact responses to treatment. The current study focused on gender as a potential moderator for affective outcomes in response to school-based mindfulness training. Sixth grade students (N = 100) were randomly assigned to either the six weeks of mindfulness meditation or the active control group as part of a history class curriculum. Participants in (...) the mindfulness meditation group completed short mindfulness meditation sessions four to five times per week, in addition to didactic instruction (Asian history). The control group received matched experiential activity in addition to didactic instruction (African history) from the same teacher with no meditation component. Self-reported measures of emotional wellbeing/affect, mindfulness, and self-compassion were obtained at pre and post intervention. Meditators reported greater improvement in emotional wellbeing compared to those in the control group. Importantly, gender differences were detected, such that female meditators reported greater increases in positive affect compared to females in the control group, whereas male meditators and control males displayed equivalent gains. Uniquely among females but not males, increases in self-reported self-compassion were associated with improvements in affect. These findings support the efficacy of school-based mindfulness interventions, and interventions tailored to accommodate distinct developmental needs of female and male adolescents. (shrink)

Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. There are no obvious incidental effects during the starting periods of CKD, patients routinely disregard to see the sickness. Early disclosure of CKD enables patients to seek helpful treatment to improve the development of this disease. AI models can effectively assist clinical with achieving this objective on account of their fast and exact affirmation execution. In this appraisal, proposed a (...) Logistic relapse framework for diagnosing CKD. Proposed calculation like NAIVE BAYES , DECISION TREE , KSTAR , LOGISITIC , AND SVM and look at these calculation and get the most noteworthy precision .AI store, which has an enormous number of missing qualities. Missing characteristics are for the most part found, taking everything into account, clinical conditions since patients might miss a couple of assessments for various reasons. By separating the misjudgements delivered by the set up models and proposed a fused model that unites determined backslide and sporadic woods by using perceptron. (shrink)

The health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases (...) with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. (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)

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)

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)

Abstract: In recent years, the outbreak of various poultry diseases has posed a significant threat to the global poultry industry. Therefore, the accurate and timely detection of chicken diseases is critical to reduce economic losses and prevent the spread of diseases. In this study, we propose a method for classifying chicken diseases using a convolutional neural network (CNN). The proposed method involves preprocessing the chicken images, building and training a CNN model, and evaluating the performance of the model. The (...) dataset used in this study comprises chicken images of three different diseases: Newcastle disease, avian influenza, and infectious bursal disease. The proposed method achieved an overall F1-score of 99.04% in classifying the chicken diseases. The results demonstrate the effectiveness of using CNN in classifying chicken diseases and provide a promising approach for early detection and diagnosis of poultry diseases. (shrink)

There are many eye diseases but the most two common retinal diseases are Age-Related Macular Degeneration (AMD), which the sharp, central vision and a leading cause of vision loss among people age 50 and older, there are two types of AMD are wet AMD and DRUSEN. Diabetic Macular Edema (DME), which is a complication of diabetes caused by fluid accumulation in the macula that can affect the fovea. If it is left untreated it may cause vision loss. Therefore, early (...) detection of diseases is a critical importance. Our main goal is to help doctors detect these diseases quickly before reaching a late stage of the disease. In ophthalmology, optical coherence tomography (OCT) is critical for evaluating retinal conditions. OCT is an imaging technique used to capture high-resolution cross-sections of the retinas of patient. In this thesis, we review ways and techniques to use deep learning classification of the optical coherence tomography images of diseases from which a Retinal is suffering. The models used to improve patient care are (VGG-16, MobileNet, ResNet-50, Inception V3, and Xception) to reduce costs and allow fast and reliable analysis in large studies. The obtained results are encouraging, since the best model ResNet-50 reaching 96.21% of testing accuracy, which is very useful for doctors, to diagnose retinal diseases. (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)

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)

Myocardial disarray is the screening tool for HCM (hypertrophy cardiomyopathy). It is also found in hypertension, congenital heart disease, corpulmonale, etc. Many patients died from heart failure due to myocardial disarray. The risk of premature death may be determined by the degree of myocyte disarray. This article reviews the anatomical explanation of myocardial disarray. It also discusses the pathogenesis of the myocardial disorganization that causes heart failure. How to measure myocardial disarray has also been assessed. Therefore, early detection (...) of myocardial disarray is advised to prevent heart failure. (shrink)

Heart failure 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 918 samples with 11 features, such as age, sex, chest pain type, resting blood pressure, 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 and 1 output) was trained on the dataset and achieved an accuracy of 90% and an average error of 0.009. 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)

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, (...) optimized through techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

The world's leading cause of morbidity and death is cardiovascular diseases (CVD), which makes early detection essential for successful treatments. This study investigates how optimization techniques can be used with machine learning (ML) algorithms to forecast cardiovascular illnesses more accurately. ML models can evaluate enormous datasets by utilizing data-driven techniques, finding trends and risk factors that conventional methods can miss. In order to increase prediction accuracy, this study focuses on adopting different machine learning algorithms, including Decision Trees, Random (...) Forest, Support Vector Machines, and Neural Networks, that have been tuned using strategies including hyper parameter selection, crossvalidation, and feature selection. (shrink)

The world's leading cause of morbidity and death is cardiovascular diseases (CVD), which makes early detection essential for successful treatments. This study investigates how optimization techniques can be used with machine learning (ML) algorithms to forecast cardiovascular illnesses more accurately. ML models can evaluate enormous datasets by utilizing data-driven techniques, finding trends and risk factors that conventional methods can miss. In order to increase prediction accuracy, this study focuses on adopting different machine learning algorithms, including Decision Trees, Random (...) Forest, Support Vector Machines, and Neural Networks, that have been tuned using strategies including hyper parameter selection, crossvalidation, and feature selection. (shrink)

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, (...) optimized through techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

Background: There are many reports in the mass media and scientific literature about child abuse caused by parents. Medical practitioners also are concerned about child abuse and need to grapple with the prevention and early detection of child abuse when working in medical facilities. Aim: The aim of this descriptive study was to explore the relationship between maternal and child factors contributing to child abuse. Methods: A sample of 50 multiparas (mothers with more than 1 child) in a (...) 48-bed postpartum hospital unit in Okinawa prefecture were asked to fill out an anonymous questionnaire regarding the relationship between mothers and their first child in September, 2007. The questionnaire contained 30 items of physical punishment that are quoted from “The Handbook of Correspondence to Child Abuse” (Ministry of Health, Labor and welfare in Japan), 24 items relating to maternal factors and 22 items to child factors, plus items related to mothers’ satisfaction with the health guidance given in the hospital. Data were analyzed using JMP (ver. 14.2; SAS Institute Inc., Cary, NC, U.S.). The significance level was set at 0.05. Results: Forty-one (82%) questionnaires were analyzed. Of the 41 valid responses, 19 mothers reported abusing their children. Child factors contributing to the abuse included the first child’s regression to infantile behavior, bullying younger brothers or sisters and being rough and violent to their friends. The significant maternal factor leading to abuse was the belief that mothers were irritated by their child. Conclusion: A first child’s developmental difficulties had a significant relationship with the harsh punishment by their mothers. The mothers need to understand their child’s developmental behavior and provide a favorable environment for nurturing young children. (shrink)

Detection thresholds for a small light spot were measured at various distances from the colinear inucer edges of white inducing elements on a dark background. The data show that thresholds are elevated when the target is located close to one or more inducing element(s). Threshold elevations diminish with increasing distance of the target from colinear edges and decreasing surface size of the inducing elements. gradients show the same tendencies. Tbe present observations add empirical support to the idea that illusory (...) figures are determined by local mechanisms that operate at early stages of perceptual information processing and decision. (shrink)

The task of visual search is to determine as rapidly as possible whether a target item is present or absent in a display. Rapidly detected items are thought to contain features that correspond to primitive elements in the human visual system. In previous theories, it has been assumed that visual search is based on simple two-dimensional features in the image. However, visual search also has access to another level of representation, one that describes properties in the corresponding three-dimensional scene. Among (...) these properties are three dimensionality and the direction of lighting, but not viewing direction. These findings imply that the parallel processes of early vision are much more sophisticated than previously assumed. (shrink)

Agricultural productivity is something on which Indian economy highly depends. This is the one of the reasons that disease detection in plants plays a vital role in agriculture field, as having disease in plants are unavoidable. If proper care is not taken in this area, then it causes serious effects on plants and due to which the overall agriculture yield will be affected. For instance, a disease named little leaf disease is a hazardous disease found in pine trees in (...) United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself if detected properly by identifying the symptoms of diseases can result in increased productivity. This paper presents an algorithm for image segmentation technique which is used for automatic detection and classification of plant leaf diseases. It also covers diseases classification techniques that can be used for plant leaf disease detection. Image segmentation is one of the method which will segment the raw images in to two or more clusters and the programmed algorithm will work fine in analyzing these clusters for disease classification and prediction of type of disease that a plant leaf gets affected. (shrink)

In the early twentieth century Walter Benjamin introduced the idea of epochal and ongoing progression in interaction between mind and the built environment. Since early antiquity, the present study suggests, Benjamin’s notion has been manifest in metaphors of gender in city-form, whereby edifices and urban voids have represented masculinity and femininity, respectively. At the onset of interaction between mind and the built environment are prehistoric myths related to the human body and to the sky. During antiquity gender projection (...) can be detected in western perceptions linking natural and built environments, commencing with Plato’s Atlantis and his Myth of Er, and later as a likely import of the Chinese yin-yang mythology. Culminating with the Age of Discovery, alongside advances in experiential awareness of the Earth’s sphericity, respective feminine and masculine earmarks can be detected in early modern perspicacity of the Earth’s southern and northern hemispheres. Our conceptions of natural and built environments inherently continue to contain gender traits. Yet urban voids, as the feminine face of city-form, have been severely understated in the built environment. Through design and configuration of urban voids, allegories of femininity in city-form ought to be celebrated, not discarded. (shrink)

Experiments are presented showing that visual search for Mueller-Lyer (ML) stimuli is based on complete configurations, rather than component segments. Segments easily detected in isolation were difficult to detect when embedded in a configuration, indicating preemption by low-level groups. This preemption—which caused stimulus components to become inaccessible to rapid search—was an all-or-nothing effect, and so could serve as a powerful test of grouping. It is shown that these effects are unlikely to be due to blurring by simple spatial filters at (...) early visual levels. It is proposed instead that they are due to more sophisticated processes that rapidly bind contour fragments into spatially-extended assemblies. These results support the view that rapid visual search cannot access the primitives formed at the earliest stages of visual processing; rather, it can access only higher-level, more ecologically-relevant structures. (shrink)

Abstract: Smoke detectors are critical devices for early fire detection and life-saving interventions. This research paper explores the application of Artificial Neural Networks (ANNs) in smoke detection systems. The study aims to develop a robust and accurate smoke detection model using ANNs. Surprisingly, the results indicate a 100% accuracy rate, suggesting promising potential for ANNs in enhancing smoke detection technology. However, this paper acknowledges the need for a comprehensive evaluation beyond accuracy. It discusses potential challenges, (...) such as overfitting, dataset size, and class imbalance, and presents strategies employed to address these issues. Additionally, the paper emphasizes the importance of alternative evaluation metrics, including precision, recall, and F1-score, to assess the model's performance comprehensively. Detailed information regarding the model architecture, data preprocessing, and hyperparameter tuning is provided to enhance the reproducibility of the research. The study also discusses ethical considerations and the implications of false positives and false negatives in smoke detection applications. While achieving 100% accuracy is a notable accomplishment, this paper calls for cautious interpretation and further validation in real-world settings. It concludes by highlighting areas for future research and improvement in smoke detection technology, emphasizing the need for practical deployment and the reduction of false alarms. (shrink)

It has generally been assumed that rapid visual search is based on simple features and that spatial relations between features are irrelevant for this task. Seven experiments involving search for line drawings contradict this assumption; a major determinant of search is the presence of line junctions. Arrow- and Y-junctions were detected rapidly in isolation and when they were embedded in drawings of rectangular polyhedra. Search for T-junctions was considerably slower. Drawings containing T-junctions often gave rise to very slow search even (...) when distinguishing arrow- or Y-junctions were present. This sensitivity to line relations suggests that preattentive processes can extract 3-dimensional orientation from line drawings. A computational model is outlined for how this may be accomplished in early human vision. (shrink)

Abstract: Alzheimer's disease (AD) is one of the most common types of dementia. Symptoms appear gradually and end with severe brain damage. People with Alzheimer's disease lose the abilities of knowledge, memory, language and learning. Recently, the classification and diagnosis of diseases using deep learning has emerged as an active topic covering a wide range of applications. This paper proposes examining abnormalities in brain structures and detecting cases of Alzheimer's disease especially in the early stages, using features derived from (...) medical images. The entire brain image was passed on through the transmission of Xception learning architectures. The Convolutional Neural Network (CNN) was constructed with the help of separable convolution layers that It can automatically learn general features from imaging data for classification. (shrink)

[What It’s Like, or What It’s About? The Place of Consciousness in the Material World] Summary: The book is both a survey of the contemporary debate and a defense of a distinctive position. Most philosophers nowadays assume that the focus of the philosophy of consciousness, its shared explanandum, is a certain property of experience variously called “phenomenal character,” “qualitative character,” “qualia” or “phenomenology,” understood in terms of what it is like to undergo the experience in question. Consciousness as defined in (...) terms of its phenomenal aspect is often called “phenomenal consciousness.” The major issue that occupies most thinkers is whether this phenomenal character happens to be a physical property, or whether it is rather sui generis. Those who believe the former are materialists; those who conclude the latter are dualists. As the currently dominant metaphysic is materialism – also sometimes called physicalism – the challenge appears to be to slot phenomenal properties among the physical properties that ultimately make up the world. David Chalmers argued powerfully that we can go very far in situating many mental properties in the physical world – namely, the properties that can be understood in functional terms – but that phenomenal properties resist such a treatment. Chalmers calls this “the hard problem” of consciousness. But there are also some quite powerful positive arguments for dualism. The two most influential ones are the modal argument, also offered by Chalmers, and the knowledge argument invented by Frank Jackson. Chalmers invites us to conceive of creatures that are exactly like human beings – physically, functionally, behaviorally – only bereft of phenomenal consciousness. If such creatures are conceivable, says Chalmers, they are metaphysically possible. And if they are metaphysically possible, materialism is false. Jackson, for his part, suggests we imagine Mary who has spent her entire life inside a black-and-white room and has seen the world through a black-and-white TV screen. But she also happens to know everything there is to know about the physics of color. And yet, Jackson suggests that once Mary is finally released from her room and sees a lawn outside, she learns something new: that this is what it is like to experience green color. The current work on consciousness is by and large characterized by attempts to answer these two dualistic arguments. I try to make sense of the positions within the domain of philosophy of consciousness by means of two major distinctions that mutually intersect. First, there is a distinction between dualism and materialism. An apparent third alternative currently on offer, the so-called Russellian monism, is unstable, collapsing into either dualism (panpsychism) or materialism (Russellian physicalism). Materialism comes in two main flavors: either the a posteriori physicalism, which detects an epistemic gap between phenomenal and physical truths, hence denying that the former could be derived from the latter; or the a priori physicalism, which does not acknowledge any such obstacle. The second major distinction is between phenomenism and representationalism. It’s true that Ned Block, who introduced this contrast, meant to distinguish between two kinds of materialism. But I believe that the distinction actually intersects the one between materialism and dualism. We thus arrive at a table with six slots, representing six main positions in the philosophy of consciousness: (1) dualist phenomenism (Chalmers, the early Jackson, and Tyler Burge); (2) dualist representationalism (René Descartes); (3) aposteriori materialist phenomenism (Block); (4) a posteriori materialist representationalism (Michael Tye, Fred Dretske, David Rosenthal); (5) a priori materialist phenomenism (David Lewis); and (6) a priori materialist representationalism (Daniel Dennett, Derk Pereboom). However, this scheme is in fact somewhat misleading. It is true that Dennett is usually classified as an apriori materialist (or, more precisely an apriori materialist representationalist), but I believe that needs to be corrected. In order to understand why, I first analyze varieties of materialist representationalism in detail, in particular various construals of phenomenal character in terms of representation, or intentionality, which includes a discussion of the identity of its content (the issue of externalism). By contrast, Dennett rejects the concept of phenomenal character. Consciousness has no intrinsic, publicly inaccessible properties. On that ground, Dennett builds an empirical, fully functionalist theory of consciousness, which he also tries to integrate within a general Darwinian framework. From that point of view, one can contrast Dennettian and representationalist views on the issue of animal consciousness. In addition to his rejection of phenomenal character, Dennett also abstains from the regular metaphysical departure point of regular materialism. He does not so much ask how an enigmatic property of consciousness fits an antecedently characterized world, but rather how far we can investigate all aspects of the world, including consciousness, using the scientific method. He is thus a methodological naturalist, rather than a metaphysical materialist. While this approach removes obstacles to the science of consciousness, it does not solve what might be called “the hardest problem” – of intentionality, not phenomenal consciousness. The hardest problem consists in the fact that our intentional discourse involves conflicting commitments that prevent a coherent metaphysic of representational states. However, it does not follow that we should give up on this discourse as a theoretical means of reduction as well as a practical tool of explanation. But it might be that intentional discourse is a somewhat pseudo one. (shrink)

The current assessment of behaviors in the inventories to diagnose autism spectrum disorders (ASD) focus on observation and discrete categorizations. Behaviors require movements, yet measurements of physical movements are seldom included. Their inclusion however, could provide an objective characterization of behavior to help unveil interactions between the peripheral and the central nervous systems. Such interactions are critical for the development and maintenance of spontaneous autonomy, self-regulation and voluntary control. At present, current approaches cannot deal with the heterogeneous, dynamic and stochastic (...) nature of development. Accordingly, they leave no avenues for real-time or longitudinal assessments of change in a coping system continuously adapting and developing compensatory mechanisms. We offer a new unifying statistical framework to reveal re-afferent kinesthetic features of the individual with ASD. The new methodology is based on the non-stationary stochastic patterns of minute fluctuations (micro-movements) inherent to our natural actions. Such patterns of behavioral variability provide re-entrant sensory feedback contributing to the autonomous regulation and coordination of the motor output. From an early age, this feedback supports centrally driven volitional control and fluid, flexible transitions between intentional and spontaneous behaviors. We show that in ASD there is a disruption in the maturation of this form of proprioception. Despite this disturbance, each individual has unique adaptive compensatory capabilities that we can unveil and exploit to evoke faster and more accurate decisions. Measuring the kinesthetic re-afference in tandem with stimuli variations we can detect changes in their micro-movements indicative of a more predictive and reliable kinesthetic percept. Our methods address the heterogeneity of ASD with a personalized approach grounded in the inherent sensory-motor abilities that the individual has already developed. (shrink)

Search in an environment with an uncertain distribution of resources involves a trade-off between exploitation of past discoveries and further exploration. This extends to information foraging, where a knowledge-seeker shifts between reading in depth and studying new domains. To study this decision-making process, we examine the reading choices made by one of the most celebrated scientists of the modern era: Charles Darwin. From the full-text of books listed in his chronologically-organized reading journals, we generate topic models to quantify his local (...) (text-to-text) and global (text-to-past) reading decisions using Kullback-Liebler Divergence, a cognitively-validated, information-theoretic measure of relative surprise. Rather than a pattern of surprise-minimization, corresponding to a pure exploitation strategy, Darwin’s behavior shifts from early exploitation to later exploration, seeking unusually high levels of cognitive surprise relative to previous eras. These shifts, detected by an unsupervised Bayesian model, correlate with major intellectual epochs of his career as identified both by qualitative scholarship and Darwin’s own self-commentary. Our methods allow us to compare his consumption of texts with their publication order. We find Darwin’s consumption more exploratory than the culture’s production, suggesting that underneath gradual societal changes are the explorations of individual synthesis and discovery. Our quantitative methods advance the study of cognitive search through a framework for testing interactions between individual and collective behavior and between short- and long-term consumption choices. This novel application of topic modeling to characterize individual reading complements widespread studies of collective scientific behavior. (shrink)

What is the rationale for the methodological innovations of experimental philosophy? This paper starts from the contention that common answers to this question are implausible. It then develops a framework within which experimental philosophy fulfills a specific function in an otherwise traditionalist picture of philosophical inquiry. The framework rests on two principal ideas. The first is Frank Jackson’s claim that conceptual analysis is unavoidable in ‘serious metaphysics’. The second is that the psychological structure of concepts is extremely intricate, much more (...) so than early practitioners of conceptual analysis had realized. This intricacy has implications for the activity of analyzing concepts: while the central, coarser, more prominent contours of a concept may be identified from the armchair, the finer details of the concept’s structure require experimental methods to detect. (shrink)

_ Source: _Volume 28, Issue 2, pp 132 - 148 A striking feature of Thomas Hobbes’s account of political obligation is his discussion of the Fool, who thinks it reasonable to adopt a policy of selective, self-interested covenant breaking. Surprisingly, scholars have paid little attention to the potential of a psychological defense of Hobbes’s controversial claim that the Fool behaves irrationally. In this paper, I first describe Hobbes’s account of the Fool and argue that the kind of Fool most worth (...) considering is the covert, long-term Fool. Then I advance and critically assess two psychological arguments according to which the Fool’s policy of self-interested covenant breaking is prudentially irrational. The first argument holds that, taken together, the deep guilt from early-stage covenant breaking, the cumulative guilt from continued covenant breaking, and the high statistical risk of detection during high-volume covenant breaking render the Fool’s policy irrational. The second argument holds that the Fool’s policy is irrational because it puts him at risk of adopting a psychologically intolerable view of his fellow covenanters and, specifically, the extent to which they can be trusted. (shrink)

Classic change blindness is the phenomenon where seemingly obvious changes which coincide with visual disruptions (such as blinks or brief blanks) go unnoticed by an attentive observer. Some early work into the causes of classic change blindness suggested that any pre-change stimulus representation is overwritten by a representation of the altered post-change stimulus, preventing change detection. However, recent work revealed that even when observers do maintain memory representations of both the pre- and post-change stimulus states, they can still (...) miss the change, suggesting that change blindness can also arise from a failure to compare the stored representations. Here we study slow change blindness, a different but related phenomenon that occurs even in the absence of visual disruptions when the change occurs sufficiently slowly, to determine whether it could be explained by conclusions from classic change blindness. Across three different slow change blindness experiments we found that observers, who consistently failed to notice the change, had access to at least two memory representations of the changing display. One representation was precise but short-lived: a relatively detailed representation of the more recent states of slow-change display, but fragile. The other representation lasted longer but was fairly general: stable but too coarse to differentiate the various stages of the changing display. These findings suggest that although multiple representations are formed, the failure to compare hypothesis might not explain slow change blindness; even if a comparison were made, the representations formed would be either too sparse (longer term stores) or too fragile (short-lived stores) for such a comparison to be informative about a change. (shrink)

In this paper I argue that the detection of emotional expressions is, in its early stages, informationally encapsulated. I clarify and defend such a view via the appeal to data from social perception on the visual processing of faces, bodies, facial and bodily expressions. Encapsulated social perception might exist alongside processes that are cognitively penetrated, and that have to do with recognition and categorization, and play a central evolutionary function in preparing early and rapid responses to the (...) emotional stimuli. (shrink)

Diabetic ketoacidosis is a serious, medical emergency that can be fatal but treatable, we aimed to evaluate the clinical profile of patients admitted with diabetic ketoacidosis. This case series study enrolled 213 non-pregnant adult and adolescent patients admitted with diabetic ketoacidosis at Tripoli Diabetes Hospital from January to September 2023. Demographic data, clinical characteristics, laboratory findings, precipitating factors, and patient outcomes were extracted from medical records and analyzed. Type 1 diabetes mellitus was present in 187 (87.8%) of patients, the age (...) range 11-84 years, (30.26±13.28), with 130 patients ≤30 years old (61.0%), females accounting for 110 of the patients (51.6%), 109 had a diabetes duration of less than 10 years (51.2%). The most common precipitating factor was insulin omission 92 (43.2%) in patients with known diabetes mellitus, whereas, diabetic ketoacidosis as the first presentation of diabetes mellitus in 24 patients (11.7%), systolic blood pressure ranged 50-160 mmHg (112.82±16.19), diastolic blood pressure varied 30-100 mmHg (70.24±11.32). Plasma glucose at presentation ranged 183-1494 mg/dl (462.92±169.85), with Eu-glycemic diabetic ketoacidosis 100-249 mg/dl were in nine patients (4.2%), whereas most of cases (135 patients, 63.4%) present with plasma glucose 250-500 mg/dl, while hyperosmolar status (≥701 mg/dl) were present in 13 patients (6.1%). Venous pH varied from 6.7-7.42 (7.13±0.14), 132 (62.0%) patients presented with pH <7.24, while severe acidosis was pH < 7 in 32 of patients (14.6%), serum bicarbonate with 10.93±4.95, severe (<5 mmol) in 91 patients (42.7%) and moderate (5-10 mmol) were in 79 patients (37.08%). The mean length of hospital stay was 3.33 days, with an average of 2.11 days spent in the high-dependent unit. Serum potassium varied from 2.4-6.7 meq/L with 3.66± 0.63, most common complications of diabetic ketoacidosis treatment were hypokalemia observed in 82 (35.7%) of cases, and hypoglycemia was detected in 41 patients (19.2%). 170 patients were discharged in good condition (79.8%), and 17.8% of patients were transferred to another hospital to receive further management for co-morbid diseases with diabetes after controlling their hyperglycemic crises. The majority of patients presented with the critical status of diabetic ketoacidosis. The findings emphasize the importance of patient education about prevention measures prompt presentation to the hospital, and clinicians' awareness for early and aggressive treatment of hyperglycemic crises. (shrink)

Extensive scientific, philosophical and artistic activities were carried out in the Islamic World’s various science and civilization centers during the early Middle Ages. In these centers, noteworthy works of mathematics, astronomy, geography, medicine, pharmacology, optics, botany, chemistry and other fields of science, which would later determine improvement paths for these fields, were created. Abu al-Izz Ismail ibn al-Razzaz al-Jazari (12th-13th centuries), was a magnificent Muslim scientist known for his work named The Book of Knowledge of Ingenious Mechanical Devices (Kitab (...) fi ma‘rifati’l-hiyal al-handasiyya) and he was born in geography where many scientists were trained. He contributed to the development of science and civilization, the joint legacy of humanity and demonstrated his true capability and success in the field of ilm al-hiyal (the scientific field which addresses the information and practices regarding mechanical technology and engineering in Islamic science history). From this perspective, it is evident that al-Jazari had a wide field of interest, made his mark in the age he lived in with his brave and innovative ideas, and was a wise engineer. al-Jazari’s detailed descriptions and explanations of water clocks, oil lamp clocks and water pumping machines, as well as their construction, were astounding. In addition, al-Jazari’s studies on metal casting techniques, cone valves, wheel balance and the real-world use of aspiration tubes are important in contemporary engineering studies. When all these are considered, it becomes clear that his designation as the “Leonardo da Vinci of the Islamic World” is not a coincidence. Many studies on al-Jazari’s scientific legacy have been conducted in various parts of the world. There are various manuscripts of his aforementioned book. Based on these manuscripts, the work has been -fully or partially- translated into many languages, including Turkish. The German and English translations served as a basis for studies on al-Jazari in other languages. In recent years, studies conducted on al-Jazari in Türkiye (books, articles, domestic and international symposiums, congresses etc.) have demonstrated his brilliance and competence as a scientist. Furthermore, studies on al-Jazari are still ongoing in Türkiye. As for Russia, the scientific legacy of the scientists raised in the Islamic World such as Avicenna, al-Farabi, Abu Bakr al-Razi, Abu Rayhan al-Biruni, Abu al-Qasim al-Zahrawi, Muhammad ibn Musa al-Khwarizmi, al-Battani, al-Kashi, Abd al-Rahman al-Khazini, Nasir al-Din al-Tusi was researched within the scope of a special plan and program. Depending on the relevant studies, al-Jazari and his work drew attention as well. A small portion of the aforementioned Russian studies is known in Türkiye. It is safe to assume that al-Jazari studies in Russia are virtually unknown in Türkiye. The present study primarily aims to contribute to the literature on the scientific legacy of al-Jazari, a Middle Age Islamic scientist best known for his famous work in the mechanical field. More specifically, the present study aims to: 1) to identify the studies on al-Jazari in Russia, and in the former USSR countries, where academic studies are still in Russian after the Soviet Union period, to create a chronological biography and to set forth their compilation; in other words, to create a bibliography which includes Russian studies on al-Jazari; 2) to provide information on the structure and content of the studies in question, to detect the methodological issues encountered in these works, and make solution recommendations to solve these issues; 3) to contribute to continuing and future studies in Türkiye on al-Jazari in terms of source and material index. In conclusion, it should be stated that comparative reading was conducted centered on The Book of Knowledge of Ingenious Mechanical Devices (Kitab fi ma‘rifati’l-hiyal al-handasiyya). It should also be noted that Russian popular internet publications (which are not based on academic research and are created/reproduced for commercial and personal purposes) were excluded from the assessment. (shrink)

Section 1 of this article presents a mathematical analysis of the longterm global urbanization dynamics and demonstrates that it could be described as a series of phase transitions between attraction basins. This makes it possible to suggest new approaches to the analysis of global social macroevolution. Section 2 presents a threestage model of the macroevolution of the World System statehood (early – developed – mature state) that, we believe, describes the main features of political macroevolution better than the twostage (...) model proposed by Claessen and Skalník (early – mature state). This model has been used as a basis for the analysis of changes in the development of political structures from the prestate polities and the most primitive early states (and their analogues) till the modern supercomplex states. In this section we also analyze the relationship between the growth of the territory controlled by the World System states and the growth of the statehood complexity. Finally, Section 3 attempts at the detection of the correlation between the global evolution of political structures and global urbanization processes. This study confirms the presence of a system of attraction basins and phase transitions within the World System macroevolutionary development field, as well as the presence of mathematically interpretable correlations and interrelations between the analyzed global processes – political, urbanizing, demographic, technological, and sociostructural. (shrink)

The current rise of neurodevelopmental disorders poses a critical need to detect risk early in order to rapidly intervene. One of the tools pediatricians use to track development is the standard growth chart. The growth charts are somewhat limited in predicting possible neurodevelopmental issues. They rely on linear models and assumptions of normality for physical growth data – obscuring key statistical information about possible neurodevelopmental risk in growth data that actually has accelerated, non-linear rates-of-change and variability encompassing skewed distributions. (...) Here, we use new analytics to profile growth data from 36 newborn babies that were tracked longitudinally for 5 months. By switching to incremental (velocity-based) growth charts and combining these dynamic changes with underlying fluctuations in motor performance – as the transition from spontaneous random noise to a systematic signal – we demonstrate a method to detect very early stunting in the development of voluntary neuromotor control and to flag risk of neurodevelopmental derail. (shrink)