Search

In an era dominated by digital communication, the vast amounts of data generated from social media and financial markets present unique opportunities and challenges for forecasting stock market prices. This paper proposes an innovative approach that harnesses the power of social media sentiment analysis combined with stock market data to predict stock prices, directly addressing the critical challenges in this domain. A major challenge in sentiment analysis is the uneven distribution of data across different sentiment categories. Traditional models struggle to accurately identify fewer common sentiments (minority class) due to the overwhelming presence of more common sentiments (majority class). To tackle this, we introduce the Off-policy Proximal Policy Optimization (PPO) algorithm, specifically designed to handle class imbalance by adjusting the reward mechanism in the training phase, thus favoring the correct classification of minority class instances. Another challenge is effectively integrating the temporal dynamics of stock prices with sentiment analysis results. Our solution is implementing a Transductive Long Short-Term Memory (TLSTM) model that incorporates sentiment analysis findings with historical stock data. This model excels at recognizing temporal patterns and gives precedence to data points that are temporally closer to the prediction point, enhancing the prediction accuracy. Ablation studies confirm the effectiveness of the Off-policy PPO and TLSTM components on the overall model performance. The proposed approach advances the field of financial analytics by providing a more nuanced understanding of market dynamics but also offers actionable insights for investors and policymakers seeking to navigate the complexities of the stock market with greater precision and confidence.

In today’s advanced network and digital age, the Internet of Things network is experiencing a significant growing trend and, due to its wide range of services and network coverage, has been able to take a special place in today’s technology era. Among the applications that can be mentioned for this network are the field of electronic health, smart residential complexes, and a wide level of connections that have connected the inner-city infrastructure in a complex way to make it smart. The notable and critical issue that exists in this network is the extent of the elements that make up the network and, due to this, the strong and massive data exchanges at the network level. With the increasing deployment of the Internet of Things, a wide range of challenges arise, especially in the discussion of establishing network security. Regarding security concerns, ensuring the confidentiality of the data being exchanged in the network, maintaining the privacy of the network nodes, protecting the identity of the network nodes, and finally implementing the security policies required to deal with a wide range of network cyber threats are of great importance. A fundamental element in the security of IoT networks is the authentication process, wherein nodes are required to validate each other’s identities to ensure the establishment of secure communication channels. Through the enforcement of security prerequisites, in this study, we suggested a security protocol focused on reinforcing security characteristics and safeguarding IoT nodes. By utilizing the security features provided by Elliptic Curve Cryptography (ECC) and employing the Elliptic Curve Diffie–Hellman (ECDH) key-exchange mechanism, we designed a protocol for authenticating nodes and establishing encryption keys for every communication session within the Internet of Things. To substantiate the effectiveness and resilience of our proposed protocol in withstanding attacks and network vulnerabilities, we conducted evaluations utilizing both formal and informal means. Furthermore, our results demonstrate that the protocol is characterized by low computational and communication demands, which makes it especially well-suited for IoT nodes operating under resource constraints.

Occupancy rate refers to the level of usage and presence of individuals within a building or a specific space. This factor can have a significant impact on building energy consumption. When the occupancy rate in a building is high, naturally, energy consumption also increases. This correlation might be due to the increased use of lighting, heating, and cooling, higher numbers of electrical and electronic devices, and similar factors associated with the presence of people in the building. One of the modern methods in the energy field involves empirically utilizing occupancy monitoring tools in buildings and analyzing the relationship between such utilization and building energy consumption through artificial neural network tools. In this research, a camera sensitive to entry and exit was installed at the entrance of an office building in Tehran, Iran. By doing so, the rate of entry and exit was accurately monitored. In the next stage, by investigating the impact of this entry and exit rate on the building's energy consumption, the energy consumption amount was predicted using an artificial neural network and a statistical method (moving average). The results indicate errors of 9.8 and 4.5 for the respective methods, highlighting that the artificial neural network yields the most accurate outcomes. Moreover, the study's findings suggest a direct correlation: as occupancy rates increase, the predicted energy consumption values also rise.

Sustainability is one of the main concerns of decision makers, factories, and retailers. This importance increases when the organization needs to define, implement, and manage a sustainable portfolio to succeed in today’s environment of change and uncertainty. Therefore, this study examines the portfolio of palm producers from 2010 to 2020. The proposed optimization model is based on the classic mean-variance approach and companies ranked using the PROMETHEE method. Our findings indicate that the effects of this asset allocation change in favour of high-score sustainability investments significantly influence risk-adjusted returns of portfolios. According to the finding, sustainable portfolios perform better than conventional ones. The findings could be useful to private and institutional investors and fund managers in constructing and managing their portfolios. The efficient frontier of the best 50% portfolio shows a higher risk for a higher return. This could explain that a higher sustainability score in different criteria leads to more optimal portfolios in terms of risk and return. Intended contributions include considering sustainability, minimizing risk, and portfolio management simultaneously.

Technology forecasting is an important and critical issue that determines the starting point of planning and is considered as a management tool directly related to the future. In the previous research items, the development of renewable energy technologies was of concern. Moreover, due to the increasing need of countries to produce electricity and facing the lack of resources, this research focuses on forecasting photovoltaic technology. Accordingly, in this paper, for technological research in the field of solar energy, the patents extracted from one of the most famous renewable energy databases in the United States (US patent database) between 200 and 2020 were examined. Next, research gaps were analyzed by using the artificial neural network clustering method and also by analyzing covered and uncovered compounds. The results show that in the future, photovoltaic technology research will move towards the third generation of technology (organic materials) as well as focus on environmental parameters and their effects on the performance of photovoltaic systems.

Today, due to the increase in people’s awareness of environmental issues and the strict policies of governments, the competitiveness of companies depends on considering environmental issues at all levels of the supply chain. However, the implementation of green supply chain management strategies has lots of different risks. The main contribution of this research is to evaluate and rank the companies in the tire industry with an emphasis on the environmental risks of the sustainable supply chain using the hybrid best-worst method (BWM) and fuzzy VIKOR (FVIKOR). First, data analysis was implemented by applying the BWM technique, which has higher reliability than other similar techniques. Next, the importance of the indicators involved in the risk of the green supply chain, including operational, supply, product return, financial, demand, organizational, and government, was calculated. Finally, according to the calculated weights for each criterion, five active companies in the tire industry were ranked using the FVIKOR technique. The results of prioritizing criteria and subcriteria showed that “financial risks” are the most important indicator among the indicators involved in green supply chain risk. Among the subcriteria, “rates related to inflation and currency” from the cluster of financial risks were recognized as the most important subcriteria. Moreover, the results of the ranking of five companies in the tire industry indicated that Dana Company is in the best situation in terms of green supply chain risks. Finally, a series of practical suggestions for managers and a series of scientific suggestions for future research have been presented.