搜索结果：Iran

The recent escalation of the Iran Israel USA conflict in 2026 has triggered widespread global discussions across social media platforms. As people increasingly use these platforms for expressing opinions, analyzing public sentiment from these discussions can provide valuable insights into global public perception. This study aims to analyze global public sentiment regarding the Iran Israel USA conflict by mining user-generated comments from YouTube news channels. The work contributes to public opinion analysis by introducing a privacy preserving framework that combines topic wise sentiment analysis with modern deep learning techniques and Federated Learning. To achieve this, approximately 19,000 YouTube comments were collected from major international news channels and preprocessed to remove noise and normalize text. Sentiment labels were initially generated using the VADER sentiment analyzer and later validated through manual inspection to improve reliability. Latent Dirichlet Allocation (LDA) was applied to identify key discussion topics related to the conflict. Several transformer-based models, including BERT, RoBERTa, XLNet, DistilBERT, ModernBERT, and ELECTRA, were fine tuned

Minors are at risk of myriad harms online, yet online dating romance scams are seldom considered one of them. While research of romance scams in Western countries finds victims to predominantly be middle-age, it is unknown if minors in geographic regions with cultural norms around teenage marriage are uniquely susceptible to online dating romance scams. We present an interview study with 16 victims of online dating romance scams in Iran who were minors when scammed. Findings show that, with westernized dating apps banned in Iran, scammers find teenage victims through messaging platforms tethered to local neighborhoods, offering relief for parental pressures around finding a marital partner and academic performance. Using threats, lies, and exploitation of emotional attachment lacking from their families, scammers pressured minors into financial and sexual favors. The study demonstrates how local cultural context should be foregrounded in future research on, and solutions for, technology-mediated harm against minors. Content Warning: This paper discusses sexual abuse.

Innovation is becoming ever more pivotal to national development strategies but measuring and comparing innovation performance across nations is still a methodological challenges. This research devises a new time-series similarity method that integrates Seasonal-Trend decomposition (STL) with Fast Dynamic Time Warping (DTW) to examine Irans innovation trends by comparison with its regional peers. Owing to data availability constraints of Global Innovation Index data , research and development spending as a proportion of GDP is used as a proxy with its limitations clearly noted. Based on World Bank indicators and an Autoencoder based imputation technique for missing values, the research compares cross-country similarities and determines theme domains best aligned with Irans innovation path. Findings indicate that poverty and health metrics manifest the strongest statistical similarity with R and D spending in Iran, while Saudi Arabia, Oman, and Kuwait show the most similar cross country proximity. Implications are that Iranian innovation is more intrinsically connected with social development dynamics rather than conventional economic or infrastructure drivers, with region-specific

This paper studies the long-run economic and institutional consequences of Iran's confrontation with the West, treating the 2006-2007 strategic shift as the onset of a sustained confrontation regime rather than a discrete sanctions episode. Using synthetic control and generalized synthetic control methods, I construct transparent counterfactuals for Iran's post-confrontation trajectory from a donor pool of countries with continuously normalized relations with the West. I find large, persistent losses in real GDP and GDP per capita, accompanied by sharp declines in foreign direct investment, trade integration, and non-oil exports. These economic effects coincide with substantial and durable deterioration in political stability, rule of law, and control of corruption. Magnitude calculations imply cumulative output losses comparable to civil-war settings, despite the absence of internal armed conflict. The results highlight confrontation as a deep and persistent economic and institutional shock, extending the literature beyond short-run sanctions effects to sustained geopolitical isolation.

This study investigates the multifaceted factors influencing wildfire risk in Iran, focusing on the interplay between climatic conditions and human activities. Utilizing advanced remote sensing, geospatial information system (GIS) processing techniques such as cloud computing, and machine learning algorithms, this research analyzed the impact of climatic parameters, topographic features, and human-related factors on wildfire susceptibility assessment and prediction in Iran. Multiple scenarios were developed for this purpose based on the data sampling strategy. The findings revealed that climatic elements such as soil moisture, temperature, and humidity significantly contribute to wildfire susceptibility, while human activities-particularly population density and proximity to powerlines-also played a crucial role. Furthermore, the seasonal impact of each parameter was separately assessed during warm and cold seasons. The results indicated that human-related factors, rather than climatic variables, had a more prominent influence during the seasonal analyses. This research provided new insights into wildfire dynamics in Iran by generating high-resolution wildfire susceptibility maps u

The main objective of this research is to identify and classify the opinions of Persian-speaking Twitter users related to drought crises in Iran and subsequently develop a model for detecting these opinions on the platform. To achieve this, a model has been developed using machine learning and text mining methods to detect the opinions of Persian-speaking Twitter users regarding the drought issues in Iran. The statistical population for the research included 42,028 drought-related tweets posted over a one-year period. These tweets were extracted from Twitter using keywords related to the drought crises in Iran. Subsequently, a sample of 2,300 tweets was qualitatively analyzed, labeled, categorized, and examined. Next, a four-category classification of users` opinions regarding drought crises and Iranians' resilience to these crises was identified. Based on these four categories, a machine learning model based on logistic regression was trained to predict and detect various opinions in Twitter posts. The developed model exhibits an accuracy of 66.09% and an F-score of 60%, indicating that this model has good performance for detecting Iranian Twitter users' opinions regarding drought

Internet censorship in the Islamic Republic of Iran restricts access to global platforms and services, forcing users to rely on circumvention technologies such as VPNs, proxies, and tunneling tools. This report presents findings from a mixed-methods study of 660 Iranian internet users, with a focus on gamers as a digitally literate and socially networked community. Survey data are combined with network measurements of latency and VPN performance to identify both technical and social strategies of circumvention. Results show that while younger users report higher confidence with circumvention, peer networks, rather than formal training, are the strongest predictors of resilience. Gaming communities, particularly those active on platforms such as Discord and Telegram, serve as hubs for sharing tactics and lowering barriers to adoption. These findings extend existing work on usable security and censorship circumvention by highlighting the intersection of infrastructural conditions and social learning. The study concludes with design and policy implications for developers, researchers, and funders working on digital rights and information controls.

In this study, the impact of research and development (R&D) expenditures on the value added of the agricultural sector in Iran was investigated for the period 1971-2021. For data analysis, the researchers utilized the ARDL econometric model and EViews software. The results indicated that R&D expenditures, both in the short and long run, have a significant positive effect on the value added in the agricultural sector. The estimated elasticity coefficient for R&D expenditures in the short run was 0.45 and in the long run was 0.35, indicating that with a 1 percent increase in research and development expenditures, the value added in the agricultural sector would increase by 0.45 percent in the short run and by 0.35 percent in the long run. Moreover, variables such as capital stock, number of employees in the agricultural sector, and working days also had a significant and positive effect on the value added in the agricultural sector.

In mid-2025, Iran experienced a novel, stealthy Internet shutdown that preserved global routing presence while isolating domestic users through deep packet inspection, aggressive throttling, and selective protocol blocking. This paper analyzes active network measurements such as DNS poisoning, HTTP injection, TLS interception, and protocol whitelisting, traced to a centralized border gateway. We quantify an approximate 707 percent rise in VPN demand and describe the multi-layered censorship infrastructure, highlighting implications for circumvention and digital rights monitoring.

Adaptive cruise control (ACC) is a technology that can reduce fuel consumption and air pollution in the automotive industry. However, its availability in Iran is low compared to industrialized countries. This study examines the acceptance and willingness to pay (WTP) for ACC among Iranian drivers. Data from an online survey of 453 respondents were analyzed using the Technology Acceptance Model (TAM) and an ordered logit model. The results show that perceived ease of use and perceived usefulness affect attitudes toward using ACC, which in turn influence behavioral intentions. The logit model also shows that drivers who find ACC easy and useful, who have higher vehicle prices, and who are women with cruise control (CC) experience are more likely to pay for ACC. To increase the adoption of ACC in Iran, it is suggested to target early adopters, especially women and capitalists, who can influence others with their positive feedback. The benefits of ACC for traffic safety and environmental sustainability should also be emphasized.

This paper aims to present empirical analysis of Iranian economic growth from 1950 to 2018 using data from the World Bank, Madison Data Bank, Statistical Center of Iran, and Central Bank of Iran. The results show that Gross Domestic Product (GDP) per capital increased by 2 percent annually during this time, however this indicator has had a huge fluctuation over time. In addition, the economic growth of Iran and oil revenue have close relationship with each other. In fact, whenever oil crises happen, great fluctuation in growth rate and other indicators happened subsequently. Even though the shares of other sectors like industry and services in GDP have increased over time, the oil sector still plays a key role in the economic growth of Iran. Moreover, growth accounting analysis shows contribution of capital plays a significant role in economic growth of Iran. Furthermore, based on growth accounting framework the steady state of effective capital is 4.27 for Iran's economy.

Artificial Neural Networks (ANN) which are a branch of artificial intelligence, have shown their high value in lots of applications and are used as a suitable forecasting method. Therefore, this study aims at forecasting imports in OECD member selected countries and Iran for 20 seasons from 2021 to 2025 by means of ANN. Data related to the imports of such countries collected over 50 years from 1970 to 2019 from valid resources including World Bank, WTO, IFM,the data turned into seasonal data to increase the number of collected data for better performance and high accuracy of the network by using Diz formula that there were totally 200 data related to imports. This study has used LSTM to analyse data in Pycharm. 75% of data considered as training data and 25% considered as test data and the results of the analysis were forecasted with 99% accuracy which revealed the validity and reliability of the output. Since the imports is consumption function and since the consumption is influenced during Covid-19 Pandemic, so it is time-consuming to correct and improve it to be influential on the imports, thus the imports in the years after Covid-19 Pandemic has had a fluctuating trend.

The cultural heritage buildings (CHB), which are part of mankind's history and identity, are in constant danger of damage or in extreme situations total destruction. That being said, it's of utmost importance to preserve them by identifying the existent, or presumptive, defects using novel methods so that renovation processes can be done in a timely manner and with higher accuracy. The main goal of this research is to use new deep learning (DL) methods in the process of preserving CHBs (situated in Iran); a goal that has been neglected especially in developing countries such as Iran, as these countries still preserve their CHBs using manual, and even archaic, methods that need direct human supervision. Having proven their effectiveness and performance when it comes to processing images, the convolutional neural networks (CNN) are a staple in computer vision (CV) literacy and this paper is not exempt. When lacking enough CHB images, training a CNN from scratch would be very difficult and prone to overfitting; that's why we opted to use a technique called transfer learning (TL) in which we used pre-trained ResNet, MobileNet, and Inception networks, for classification. Even more, the

In this study, the relationship between burnout and family functions of the Melli Iran Bank staff will be studied. A number of employees within the organization using appropriate scientific methods as the samples were selected by detailed questionnaire and the appropriate data is collected burnout and family functions. The method used descriptive statistical population used for this study consisted of 314 bank loan officers in branches of Melli Iran Bank of Tehran province and all the officials at the bank for >5 years of service at Melli Iran Bank branches in Tehran. They are married and men constitute the study population. The Maslach Burnout Inventory in the end internal to 0/90 alpha emotional exhaustion, depersonalization and low personal accomplishment Cronbach alpha of 0/79 and inventory by 0/71 within the last family to solve the problem 0/70, emotional response 0/51, touch 0/70, 0/69 affective involvement, roles, 0/59, 0/68 behavior is controlled. The results indicate that the hypothesis that included the relationship between burnout and 6, the family functioning, problem solving, communication, roles, affective responsiveness, affective fusion there was a significant r

In order to study the relation between the components of atmospheric circulation and summer precipitation over the Iranian plateau, daily rainfall data of synoptic stations of Iran were obtained from IRIMO for the period 1970 to 2003. Composite maps of different components such as pressure levels, vector wind, U and V winds, vertical velocity, Specific humidity, OLR, streamlines, vorticity, and the temperature was drawn and analyzed. The results showed that the summer circulation of the atmosphere over South West Asia after a sudden change from late May to early June was established in its normal position. Different new pressure systems such as Zagros Trough were identified over the surface and the Turkmenistan Anticyclone, the quasi-stationary trough of east Turkey at 700 hPa level. During the establishment of Subtropical High Pressure in the middle and upper troposphere over Iran, the summer circulation of southwest Asia reached its highest intensity and expansion and continued to mid-August. On the other hand, the establishment of the Turkmenistan Anticyclone and Pakistan Low caused the 120 days winds of Sistan, and the development of High pressure over the Arabian Peninsula and

The present study aimed to forecast the exports of a select group of Organization for Economic Co-operation and Development (OECD) countries and Iran using the neural networks. The data concerning the exports of the above countries from 1970 to 2019 were collected. The collected data were implemented to forecast the exports of the investigated countries for 2021 to 2025. The analysis was performed using the Multi-Layer-Perceptron (MLP) neural network in Python. Out of the total number, 75 percent were used as training data, and 25 percent were used as the test data. The findings of the study were evaluated with 99% accuracy, which indicated the reliability of the output of the network. The Results show that Covid-19 has affected exports over time. However, long-term export contracts are less affected by tensions and crises, due to the effect of exports on economic growth, per capita income and it is better for economic policies of countries to use long-term export contracts.

Since the strategies and plans for e-commerce development are different for different industries and since the oil industry is one of the most important industries in Iran, the scope of this research is thus confined to that of the oil industry in Iran. The main aim of this study is to identify and classify the different features of e-commerce development stages and features based on the different business types present in companies in the oil industry in Iran. In order to achieve both of these objectives a questionnaire was developed and administered online. The questionnaire was distributed to forty representatives working in different companies. The collected data was classified and sorted and the priority e-commerce features was classified and displayed as triangles for each business type. Furthermore, the experts were asked to indicate the features which they implemented in their companies in order to know the most used features in each stage. The results of this study give an insight to the practice of e-commerce for Iranian oil companies and can be used to strategize future directions for the industry in terms of e- commerce.

The study aimed to examine the effect of MLIS degree on graduates in Iran from different dimensions. The study examined the effects of MLIS on scientific progress, the development of subject expertise, employment, individual characteristics, skills and capabilities, and scientific activities of Iran's graduates. The study was a descriptive-survey and researcher-made questionnaire is used for data collection. The overall effect average of degree of MLIS on Iranian graduates was equal to 3/25. The findings showed that the average effect of MLIS degree associated with each studied factors on the graduates in the country were: Scientific progress (3/13), development of subject expertise (3/27), employment (3/27), individual characteristics (2/75), skills and capabilities (3/48), scientific activities (3/57(. Discussion: Based on the results, the effect of MLIS degree on Iranian graduates was more than moderate. Generally, it can be concluded that MLIS courses at universities in the country, can increase the value of a master's degree of graduates at an acceptable level, but is not perfect; it seems that the authorities should increase their efforts to promote the value of a master's de