Poor Segmentation Research Articles

Social Protection and its Impact on the Cohesion of Vulnerable Groups: A Field Study of a Sample Social Protection Network Beneficiaries

Social protection meets different aspects of the needs of vulnerable groups, such as the economic, health, education, and family relations and ties in the Iraqi society. This is because vulnerable groups have suffered from social and economic influences that have negative implications on the social reality as a whole. Poverty is a case in point, which paved the way to frequent setbacks that have led to social structure instability. Accordingly, the present study aims to examine the role and effect of the Net of Social Protection Program in equally distributing social protection to curb or mitigate any negative consequnces that might happen to the poor segments and vulnerable people, who are succeptible to shocks, such as: the orphans, unemployed, disabled and the poor. Since such a step reduces poverty rates, the Ministry of Labor and Social Affairs has sought through its social protection network programs to achieve equality, the biggest achievement to the country. Having examined the issue, the researcher has found that there is no social justice in distributing material guarantees to the poor or the people in need. There are several individuals whose names are registered in the lists of the people in need. Such names are either fake or belong to people who do not really need such aids. According the puposes behind the present aims are to: maintain fairness in distributing cash collaterals for the people who live below the poverty line in need, overcome the difficulties of coping with the poor community, deal with those people positively, and uplift them. The study has concluded that the number of poor people is increasing in the Iraqi society. Besides, such groups of people need to be respected, severed and provided with a decent way of living. All such rights are part and parcel of human rights.

Gender-related differences in frontal-parietal modular segregation and altered effective connectivity in internet gaming disorder.

BackgroundAlthough previous studies have revealed gender-related differences in executive function in internet gaming disorder (IGD), neural mechanisms underlying these processes remain unclear, especially in terms of brain networks.MethodsResting-state fMRI data were collected from 78 subjects with IGD (39 males, 20.8 ± 2.16 years old) and 72 with recreational game use (RGU) (39 males, 21.5 ± 2.56 years old). By utilizing graph theory, we calculated participation coefficients among brain network modules for all participants and analyzed the diagnostic-group-by-gender interactions. We further explored possible causal relationships between networks through spectral dynamic causal modeling (spDCM) to assess differences in between-network connections.ResultsCompared to males with RGU, males with IGD demonstrated reduced modular segregation of the frontal-parietal network (FPN). Male IGD subjects also showed increased connections between the FPN and cingulo-opercular network (CON); however, these differences were not found in female subjects. Further spDCM analysis indicated that the causal influence from CON to FPN in male IGD subjects was enhanced relative to that of RGU males, while this influence was relatively reduced in females with IGD.ConclusionsThese results suggest poor modular segmentation of the FPN and abnormal FPN/CON connections in males with IGD, suggesting a mechanism for male vulnerability to IGD. An increased “bottom-up” effect from the CON to FPN in male IGD subjects could reflect dysfunction between the brain networks. Different mechanisms may underlie in IGD, suggesting that different interventions may be optimal in males and females with IGD.

Automatic Acetowhite Lesion Segmentation via Specular Reflection Removal and Deep Attention Network.

Automatic acetowhite lesion segmentation in colposcopy images (cervigrams) is essential in assisting gynecologists for the diagnosis of cervical intraepithelial neoplasia grades and cervical cancer. It can also help gynecologists determine the correct lesion areas for further pathological examination. Existing computer-aided diagnosis algorithms show poor segmentation performance because of specular reflections, insufficient training data and the inability to focus on semantically meaningful lesion parts. In this paper, a novel computer-aided diagnosis algorithm is proposed to segment acetowhite lesions in cervigrams automatically. To reduce the interference of specularities on segmentation performance, a specular reflection removal mechanism is presented to detect and inpaint these areas with precision. Moreover, we design a cervigram image classification network to classify pathology results and generate lesion attention maps, which are subsequently leveraged to guide a more accurate lesion segmentation task by the proposed lesion-aware convolutional neural network. We conducted comprehensive experiments to evaluate the proposed approaches on 3045 clinical cervigrams. Our results show that our method outperforms state-of-the-art approaches and achieves better Dice similarity coefficient and Hausdorff Distance values in acetowhite legion segmentation.

U-High resolution network (U-HRNet): cloud detection with high-resolution representations for geostationary satellite imagery

ABSTRACT Cloud detection is one of the important tasks for remote sensing image preprocessing. In this work, multiple infrared observation channels with high time resolution are utilized to extract cloud from geostationary satellite imagery. Compared with detected cloud mask from polar satellite data with the high spatial resolution, the detection from geostationary satellite data has high timeliness and practicability, but it is challenging for the detailed segmentation ability of the network. Due to the fact that some small clouds only occupy a few pixels in geostationary satellite images with low spatial resolution, the existing network has poor segmentation ability for these small targets. To tackle this problem, a new neural network named U-High Resolution Network (U-HRNet) is proposed for cloud detection. Combing both multi-scale feature extraction using a High Resolution Network (HRNet) architecture and merging shallow information and deep information via the skip connection (SC) of a U-Net structure, the proposed U-HRNet produces strong high-resolution representations for accurate detection of details on cloud segmentation. The accuracy of the method is evaluated by manually labelled ground truth against different methods using objective evaluation indices. The results proved the proposed U-HRNet performs well on FengYun-4A (FY-4A) images and can effectively detect incorrect areas of the cloud mask products of the National Satellite Meteorological Centre (NSMC), depicting outperformance over existing methods.

NEXUS BETWEEN FINANCIAL DEVELOPMENT AND INCOME INEQUALITY BEFORE PANDEMIC COVID-19: DOES FINANCIAL KUZNETS CURVE EXIST IN MALAYSIA, INDONESIA, THAILAND AND PHILIPPINES?

This study offers new insights for policymakers to reduce income inequality, thus ensuring economic growth which greatly benefits the poor segment of population and directing financial sector to provide easy access to financial resources for lower income group at cheaper cost. Bound test was applied to examine the long-run and short-run relationships based on the sample period beginning from 1970 until 2016. The results confirmed the existence of a long-run relationship between the variables. Financial development in Malaysia, Indonesia and Thailand had successfully reduced income inequality, however, a different effect was recorded in the Philippines where income distribution was worsened. Furthermore, economic growth brought positive effect to income distribution in Malaysia and Indonesia, but not for Thailand and the Philippines. Inflation, trade openness and foreign direct investment, provided mixed results for all countries. Among the policies recommendation for this paper are there should be more easy accessibility for entrepreneurs to reach the wide range of financial services including conventional and Islamic financial products, the expansion of capital market, as well as giving proper attention to the financial sector. Besides, granting the access to capital markets for low income groups or underprivileged individuals might be helpful to them either by developing entrepreneurial skill or involvement in productive activities and receive better salaries. This policies will gives insight to the policymakers as it the outcome is based on the spread of pandemic Covid-19 that hit the world.Keywords: Financial Development; Income Inequality; ASEAN-4; Financial Kuznets CurveJEL Classifications: G10; F62DOI: https://doi.org/10.32479/ijeep.10616

Residual-driven Fuzzy C-Means Clustering for Image Segmentation

Due to its inferior characteristics, an observed (noisy) image's direct use gives rise to poor segmentation results. Intuitively, using its noise-free image can favorably impact image segmentation. Hence, the accurate estimation of the residual between observed and noise-free images is an important task. To do so, we elaborate on residual-driven Fuzzy C-Means (FCM) for image segmentation, which is the first approach that realizes accurate residual estimation and leads noise-free image to participate in clustering. We propose a residual-driven FCM framework by integrating into FCM a residual-related fidelity term derived from the distribution of different types of noise. Built on this framework, we present a weighted $\ell_{2}$-norm fidelity term by weighting mixed noise distribution, thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown noise. Besides, with the constraint of spatial information, the residual estimation becomes more reliable than that only considering an observed image itself. Supporting experiments on synthetic, medical, and real-world images are conducted. The results demonstrate the superior effectiveness and efficiency of the proposed algorithm over existing FCM-related algorithms.

DenseNet-Based Land Cover Classification Network With Deep Fusion

Recently, fully convolutional network (FCN)-based (Long <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2015) networks have made impressive success in semantic segmentation, and these approaches achieve satisfactory results in natural images. However, in the field of high-resolution remote sensing image segmentation, the accuracy has a considerable huge gap compared with that of natural images. Through the development process of semantic segmentation, we found that the key to accurate segmentation is the context. Effective networks can always obtain large contexts, which means that context is the key to one successful segmentation network. For high-resolution remote sensing images, their elements always extend to large scope and they have no clear or regular boundaries. As a result, it needs more context to correctly classify each pixel. However, the networks designed for natural images obviously do not meet this requirement, and thus, they achieve poor segmentation results for high-resolution images. Therefore, we do some targeted improvements. Based on one powerful backbone, we add two new fusions called unit fusion and cross-level fusion, respectively. Unit fusion makes the connection from the encoder part to the decoder part not only occur in the final output of each dense block but also in the middle feature layers inside one dense block. These added fusions make feature fusion in the same level more complete, which is of great significance for complex and zigzag boundary areas. As a complement for unit fusion, cross-level fusion aims to enhance the fusion of different dense blocks. Specifically, cross-level fusion learns from the internal structure of the dense block and applies the design to the whole network level. It can incorporate nonadjacent features and rapidly increase the receptive field and context, which is very effective for the segmentation of targets with very large sizes. Experiments on Deepglobe (Demir <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2018) show significant improvements in our work.

AF-Net: A Medical Image Segmentation Network Based on Attention Mechanism and Feature Fusion

Medical image segmentation is an important application field of computer vision in medical image processing. Due to the close location and high similarity of different organs in medical images, the current segmentation algorithms have problems with mis-segmentation and poor edge segmentation. To address these challenges, we propose a medical image segmentation network (AF-Net) based on attention mechanism and feature fusion, which can effectively capture global information while focusing the network on the object area. In this approach, we add dual attention blocks (DA-block) to the backbone network, which comprises parallel channels and spatial attention branches, to adaptively calibrate and weigh features. Secondly, the multi-scale feature fusion block (MFF-block) is proposed to obtain feature maps of different receptive domains and get multi-scale information with less computational consumption. Finally, to restore the locations and shapes of organs, we adopt the global feature fusion blocks (GFF-block) to fuse high-level and low-level information, which can obtain accurate pixel positioning. We evaluate our method on multiple datasets(the aorta and lungs dataset), and the experimental results achieve 94.0% in mIoU and 96.3% in DICE, showing that our approach performs better than U-Net and other state-of-art methods.

From Papal Envoys to Martyrs of the Faith: An Attempt in Generalization of Franciscan preaching in China in the 13th– 18th Centuries

The article is an attempt to systematize the preaching of the Franciscan order in China, starting with the papal embassies to the Great Khans who conquered the Middle Empire and founded the Yuan dynasty until the end of the 20th century. The author groups the information into several major periods, suggesting a five-stage periodization of the Franciscan presence in the Far East. A change in the preaching paradigm is noted during the 700 centuries of the fickle Minorites’ presence in China. While the first reconnaissance missions, achieving modest success in preaching to non-Chinese subjects of the Mongol emperors, were mainly diplomatic in nature, in modern times the mission, enjoying the support of the Spanish Padroado system, is purposefully concentrated on preaching work, especially among the poor segments of the population. Since the 16th century begins a change in the entire logistic paradigm of the Far Eastern missionary work. If in the Middle Ages the Pope had enough to send several barefoot Franciscans to the Tatars, then in modern times the church is already forced to reckon with the countries that divided the world, initiating the Age of Exploration, first of all, with Spain and Portugal, the two then superpowers, each of which supported their own preachers, competing for influence in India, China and Japan and giving the task of preaching Christianity an additional political dimension, laden with rivalry and intrigue. The article is a continuation of the piece by the same author, focusing on theoretical foundations of the Franciscan proselytization, published earlier [Dubrovskaya, 2020(1)].

The Rapid Spread of Covid-19 Pandemic and Its Psychosocial and Economic Challenges on Motorcycle Riders in the Transport Industry in Kimili Subcounty, Bungoma County, Kenya

The spread of Covid-19 pandemic in East Africa has greatly undermined the daily economic activities of the motorbike operators in the region during its first phase. The motor bike riders (boda boda) have been condemned for the fast spread of Covid-19 due to the nature of their business of moving people from place to place and deep into communities. Boda boda transport system has been embraced by most people in East Africa since it is fast, flexible and cheap and has become more popular among the poor segments of the society. However, with the outbreak of Covid-19 pandemic the East African governments have put spotlight on the motorbike transport industry as a weak link in the fight against the Covid-19 pandemic. In Uganda for instance President Yoweri Museveni had to ban boda boda operators from carrying passengers for the fear of community-to-community spread of the pandemic. The consequences of the negative perception against the boda boda operators carried psychosocial as well as economic repercussions with it such as intense fear, obsessive behaviour, anxiety and stigma. The purpose of this study was to investigate Covid-19 pandemic and its psychosocial as well as economic challenges on the motorbike transport in Kimilili Sub County, Bungoma County, Kenya. The study population comprised of all the motorcycle riders within Kimilili town. The sample size comprised of 24 motor bike riders sampled from the 12 main town stages from which 2 participants were selected through simple random sampling. The study adopted qualitative research design where descriptive survey was employed. The study employed Focused group Discussion (FGD) tool for data collection. The FGD involved the participants in a discussion on common psychosocial problems affecting the boda boda operators. Data was collected and analyzed qualitatively then presented in prose. The results revealed that motorbike riders had faced numerous challenges ranging from reduced income and stigma to police harassment and corruption. The study recommends reforms that can restore sanity and dignity to the boda boda public transport industry such as financial empowerment, social protection, training of staff and counselling services.

Peak-Graph-Based Fast Density Peak Clustering for Image Segmentation

Fuzzy c-means (FCM) algorithm as a traditional clustering algorithm for image segmentation cannot effectively preserve local spatial information of pixels, which leads to poor segmentation results with inconsistent regions. For the remedy, superpixel technologies are applied, but spatial information preservation highly relies on the quality of superpixels. Density peak clustering algorithm (DPC) can reconstruct spatial information of arbitrary-shaped clusters, but its high time complexity $O(n^2)$ and unrobust allocation strategy decrease its applicability for image segmentation. Herein, a fast density peak clustering method (PGDPC) based on the kNN distance matrix of data with time complexity $O(nlog(n))$ is proposed. By using the peak-graph-based allocation strategy, PGDPC is more robust in the reconstruction of spatial information of various complex-shaped clusters, so it can rapidly and accurately segment images into high-consistent segmentation regions. Experiments on synthetic datasets, real and Wireless Capsule Endoscopy (WCE) images demonstrate that PGDPC as a fast and robust clustering algorithm is applicable to image segmentation.

Социальная структура общества развитого Средневековья в Томском Приобье (по материалам могильников)

Abstract: This paper is aimed at reconstructing the social structure of the population of the Tomsk Ob Region in the High Middle Ages. The source base is made up of materials from archaeological excavations of three burial grounds: Astrakhantsevsky burial mound, Basandaysky burial mound, and the burial ground at the mouth of the Malaya Kirgiska River. The composition of grave goods (in terms of quantity and quantity) and a burial structure served as criteria for the analysis. 212 graves of adults and children have been selected. As a result of the analysis, we have identified five adult groups and three children’s groups. The adult groups are as follows. Representatives of group I had the most complete set of weapons: sabers, spears, bows and arrows. Only this group of warriors had a protective suite of armour. There were horses in some graves. We identified one grave of this group in the Astrakhantsevsky burial mound, two in Basandaysky burial mound, and seven in the burial ground at the mouth of the Malaya Kirgiska River. Group II includes graves with rich goods, such as jewelry made of precious metals, precious and semiprecious stones. There are also fragments of silk. There are graves with weapons, horses or their ammunition. Group III is divided into two subgroups: III/1 — cavalry warriors, professionals; III/2 — infantry warriors, hunters and other groups of the population. Group IV includes poorer segments of the population, with a small set of household goods. Group V includes graves without goods. The children’s groups include: I — with rich goods, II — with scarce goods, III — without goods. Attention should be paid to the following circumstances: in several cases we identified persons buried in a mound as relatives or members of the same family. There are interesting observations concerning the location of mirrors. They were found in adult groups I, II, III, IV and children’s group I, II. However, no special group of people authorized to perform religious rituals was identified. This situation is likely to reflect the social reality of that period in the Tomsk Ob Region. The identified adult groups represent the social structure: group I — representatives of local military and administrative authorities; group II — rich people, group III — cavalry warriors, power base (together with representatives of group I, they are likely to constitute the military elite); group III/2 — militia in wartime and hunters, fishermen or herders in peacetime; group IV — poorer segments of the local population, small in size; group V — people without goods, the poor, possibly dependent people. Among children, group I (children of wealthy parents) and group II (children of low-income parents) stand out. As for children without goods, it is quite difficult to explain reasons for such burials. Keywords: Tomsk Ob region, High Middle Ages, archaeology, burial ground, social structure, elite, power

Благотворительность в профессиональной среде немцев Санкт-Петербурга в конце XVIII – начале XX века

The article studies the system of German professional charitable organizations that existed in the late 18th - early 20th century. Charitable associations related to professional activities are conditionally divided into three categories: 1) shelters for representatives of various professions; 2) societies and mutual aid funds; 3) associations providing various assistance to the poor segments of the city population. We analyse the underlying mechanisms and functions of philanthropic institutions created either by German charitable organizations or private individuals for representatives of certain professions; in some cases we overview their financing as well. The article provides brief look at German immigrant mutual aid funds and principles of their operations. Charitable support was especially necessary for the Germans who had recently arrived in the city and had not yet found and employment, as well to the layers of German population unable to work. By analyzing the means of inter-societal support and external charity efforts it is possible to identify and suggest possible ways to provide social assistance to foreigners who come to St. Petersburg for the purpose of professional employment.

Effect of Poverty on Services Export Concentration in Developing Countries

This paper investigates the effect of poverty on services export concentration in developing countries. The analysis has used an unbalanced panel dataset of 95 developing countries over the period 1995–2014. Findings suggest that higher poverty rates induce greater services export concentration, and this effect translates through three channels, namely, human capital, trade openness, and export product concentration. Thus, if they were to diversify services export items, policymakers should implement policies (such as reducing services trade barriers and improving the business environment) that directly promote services export diversification, but also measures that directly target poor segments of the population.

Semantic Segmentation of Sentinel-2 Imagery for Mapping Irrigation Center Pivots

Estimating the number and size of irrigation center pivot systems (CPS) from remotely sensed data, using artificial intelligence (AI), is a potential information source for assessing agricultural water use. In this study, we identified two technical challenges in the neural-network-based classification: Firstly, an effective reduction of the feature space of the remote sensing data to shorten training times and increase classification accuracy is required. Secondly, the geographical transferability of the AI algorithms is a pressing issue if AI is to replace human mapping efforts one day. Therefore, we trained the semantic image segmentation algorithm U-NET on four spectral channels (U-NET SPECS) and the first three principal components (U-NET principal component analysis (PCA)) of ESA/Copernicus Sentinel-2 images on a study area in Texas, USA, and assessed the geographic transferability of the trained models to two other sites: the Duero basin, in Spain, and South Africa. U-NET SPECS outperformed U-NET PCA at all three study areas, with the highest f1-score at Texas (0.87, U-NET PCA: 0.83), and a value of 0.68 (U-NET PCA: 0.43) in South Africa. At the Duero, both models showed poor classification accuracy (f1-score U-NET PCA: 0.08; U-NET SPECS: 0.16) and segmentation quality, which was particularly evident in the incomplete representation of the center pivot geometries. In South Africa and at the Duero site, a high rate of false positive and false negative was observed, which made the model less useful, especially at the Duero test site. Thus, geographical invariance is not an inherent model property and seems to be mainly driven by the complexity of land-use pattern. We do not consider PCA a suited spectral dimensionality reduction measure in this. However, shorter training times and a more stable training process indicate promising prospects for reducing computational burdens. We therefore conclude that effective dimensionality reduction and geographic transferability are important prospects for further research towards the operational usage of deep learning algorithms, not only regarding the mapping of CPS.

Continual improvement of nasopharyngeal carcinoma segmentation with less labeling effort.

Convolutional neural networks (CNNs) offer a promising approach to automated segmentation. However, labeling contours on a large scale is laborious. Here we propose a method to improve segmentation continually with less labeling effort. The cohort included 600 patients with nasopharyngeal carcinoma. The proposed method was comprised of four steps. First, an initial CNN model was trained from scratch to perform segmentation of the clinical target volume. Second, a binary classifier was trained using a secondary CNN to identify samples for which the initial model gave a dice similarity coefficient (DSC)<0.85. Third, the classifier was used to select such samples from the new coming data. Forth, the final model was fine-tuned from the initial model, using only selected samples. The classifier can detect poor segmentation of the model with an accuracy of 92%. The proposed segmentation method improved the DSC from 0.82 to 0.86 while reducing the labeling effort by 45%. The proposed method reduces the amount of labeled training data and improves segmentation by continually acquiring, fine-tuning, and transferring knowledge over long time spans.

Does free-market capitalism drive unequal access to health? An empirical analysis, 1970–2015

ABSTRACT Many scholars, particularly in public health, argue that neoliberal capitalist economic forces adversely affect communities by increasing inequalities, ultimately affecting health. Apparently, corporate capitalism affects health and communitarian concerns because governments place corporate profits over the publićs interests. Using unique data collected by the Varieties of Democracy (VDEM) project that capture the degree of access of the poorest segments of society to health services comparable with those available to the richest segments, this study finds that an index of economic freedom robustly reduces inequality of access to health. We argue that these results obtain because greater exposure to global markets increases the premium on the productivity of labour, increasing incentives for political elites to invest in productivity-enhancing public goods. Our results are robust to a number of alternative models and data, and robust to instrumental variables analyses addressing potential endogeneity. Rather than free-market capitalism increasing health-related neglect of society, our data suggest that free-market capitalist conditions promote equitable access to health. This is good news for governments wishing to grow their economies, reform broken health systems for gaining advantages in a competitive global economy, and serve communitarian interests, such as shared good health.

Segmentation and classification of knee joint ultrasonic image via deep learning

The knee is one of the most complicated joints in the human body, but it could be easily injured. Ultrasound imaging is an important technology for the diagnosis of the knee disease. To assist doctors in the treatment and reduce errors of judgment, we investigate the segmentation of disease regions and the automated identification of the typical knee joint diseases. First, we use deep learning to segment the Region of Interest (ROI). To solve the mis-segmentation and poor edge segmentation that occur when the ultrasound image is directly fed into the deep neural network, an image segmentation framework is proposed that integrates snake preprocessing, dilated convolution to expand the receptive fields, and multi-channel learning. Second, due to the small difference in features among various categories of ultrasound images, a hybrid algorithm is proposed based on the Resnet rough classification and quadratic training with graph embedding. Finally, the experiments show that the proposed image segmentation framework achieves 10% greater accuracy than a common segmentation network. By visualizing the feature vectors extracted from the classification network, we verify that the feature vectors are closer on similar images after quadratic training by graph embedding. Employing the optimization with quadratic training, we increase the classification accuracy by 11% compared to the Resnet approach.

MODERN PROBLEMS AND ASSESSMENT OF THE EFFECTIVENESS OF THE STATE SOCIAL POLICY AND SOCIAL SECURITY OF THE POPULATION

Purpose of the study: This article aims to analyze the theoretical and practical issues of public administration in the social sphere in a market economy; to consider the implementation of social programs of the Republic of Kazakhstan. Recommendations were also made to assess areas for improving the social system. Special attention is paid to the analysis of political activities and policies in all spheres of society.&#x0D; Methodology: The study was based on the analysis of the development of social sectors in a market economy and evaluated the directions for improving the system of social sectors in a market economy. General scientific methods of cognition-dialectical, analysis, synthesis, system and functional approaches, generalization, comparison, analogy; methods of empirical research have been employed for this study.&#x0D; Main Findings: In the article were studied and suggested ways to support the poor and low-income segments of the population, help them find employment in society, reduce the level of crime in society, develop social spheres (education, health, science, culture, residential homes, etc.), and ensure the country's environmental security.&#x0D; Applications of this study: The results of the study can be used by public authorities in the development and implementation of policies in the field of social protection and income regulation, population and development, gender, and family policy.&#x0D; Novelty/Originality of this study: The study includes the development of theoretical foundations of social protection of the population, taking into account the current rules of economic social dynamics; the development of a model for the implementation of social protection of the population related to the justification of parameters of its demographic effectiveness and the justification of new directions and practical recommendations in the development of methodological approaches for the development of social protection of young families.

Istoriya obrazovaniya v Indii v pedagogicheskoj koncepcii Dzh. P. Najka

The article describes in detail the process of formation, development and reform of education in India. The great scientific and practical contribution of the Indian scientist J.P. Naik is that in his works he revealed the priority directions for the development of education in India taking into account the interests of the poorest segments of the population. He believed that all general education institutions should devote more time to participation in social welfare and national development programmes. At the heart of the education system, the scientist singled out a humanistic basis.