Journal of Computer and Robotics
Volume:17 Issue: 1, Winter and Spring 2024

Prediction is a very important problem that appears in many disciplines. Better weather forecasts can save lives in the event of a catastrophic hurricane; better financial forecasts can improve the return on an investment. The increasing rate of industrial development and urbanization, especially in developing countries, has led to increased levels of air pollution along with increased concern about air pollution effect on human health. This has taken about a diversity of strategies for air quality management, prediction and pollution control. Today’s applications of fuzzy systems are emerging in uncertain environments such as air quality assessments. A fuzzy system that accounts for all of the uncertainties that are present, namely, rule uncertainties due to training with noisy data and measurement uncertainties due to noisy measurements that are used during actual forecasting. The performance results on real data set show the superiority of the fuzzy-markov model in the prediction process with an average accuracy of 94.79% compared to other related works. These results are promising for early prediction of the natural disasters and prevention of its side effects

During the corona days, the body's activities are very reduced due to the quarantine, and after that, many people still have little activity. Nearly two billion people in the world are overweight. In other words, more than 30% of the world's population is obese or overweight. In this study, a solution for fitness and weight loss at home has been proposed. 2 groups participated in this study, the first group consisted of 20 people in a traditional way and the second group included 20 people under virtual reality, all of whom were undergraduate students, for 4 weeks and 3 sessions per week participated in this study and none of them had experience using virtual reality. The results show that fitness parameters include waist circumference, weight, BMI and the distance traveled in the Cooper test have improved. The motivation of people to continue this study was more in the virtual reality group than in the normal group.

Classification is the operation of dividing various data into multiple classes where they share quantitative and qualitative similarities. Classification has many use cases in engineering fields such as cloud computing, power distribution, and remote sensing. The accuracy of many classification techniques such as k-nearest neighbor (k-NN) is highly dependent on the method used in the calculation of distances between samples. It is assumed that samples close to each other belong to the same class while samples that belong to different classes have a large distance between them. One of the popular distance calculation methods is the Mahalanobis distance. Many methods, including large margin nearest neighbor (LMNN), have been proposed to improve the performance of k-NN in recent years. Our proposed method aims to introduce a cost function to calculate data similarities while solving the local optimum pitfall of LMNN and optimizing the cost function determining distances between instances. Although k-NN is an efficient classification technique that is simple to comprehend and use, it is costly to compute for large datasets and sensitive to outlier data. Another difficult feature of k-NN is that it can only measure distance in Euclidean space. The distance metric should ideally be modified to fit the specific needs of the application. Due to the disadvantages in k-NN and LMNN methods, to optimize the objective function to calculate distances for the test data and to improve classification accuracy, we initially use the genetic algorithm to reduce the range of the solution space and then by using the gradient descent the optimal values of parameters in the cost function is obtained. Our method is carried out on different benchmark datasets with varying numbers of attributes and the results are compared to k-NN and LMNN methods. Misclassification rate, precision, f1 score, and kappa score are calculated for different values of k, mutation rate, and crossover rate. Overall, our proposed method shows superior performance with an average accuracy rate of 87.81% which is the highest among all methods. The average precision, f1 score, and kappa score of our method are 0.8453, 0.8513, and 0.6976 respectively.

Outsourcing of corporate activities by suppliers has long been done in the oil and gas industry. Outsourcing is known as a tool to gain strategic advantages. Outsourcing maintenance is also a common practice in many industries, including producing chemicals, petroleum, petrochemicals, and medical equipment. However, this process involves many risks, with their extent and nature still unclear. There are strong reasons for outsourcing some of the most important economic concepts. Determining the effective indicators in this selection and the importance and priority of each of them has always been the subject of intense research. In this paper, we examined the effects of these variables and assessed their relationship with decision-making outsourcing maintenance at gas refineries. First, the effective variables were identified by reviewing the literature and based on experts’ opinions. Next, it was tried to prioritize the indicators identified from previous studies using the relatively new Bayesian Best-Worst method (BWM). The results are then compared using one of the most recent decision-making methods, i.e., the Ordinal Priority Approach. Comparing the results of these two models shows that in both models, the cost of technology modernization and upgrades, the cost of emergency repairs and production stops, the cost of depreciation of equipment and machinery, and the cost of major repairs are the top four significant criteria among all the examined ones. However, the first and second methods consider “cost of maintenance” and “cost of productivity” more significant, respectively. It is worth noting that other differences were also identified in this study.

Semantic Textual Similarity (STS) is considered one of the subfields of natural language processing that has gained extensive research attention in recent years. Measuring the semantic similarity between words, phrases, paragraphs, and documents plays a significant role in natural language processing and computational linguistics. Semantic Textual Similarity finds applications in plagiarism detection, machine translation, information retrieval, and similar areas. STS aims to develop computational methods that can capture the nuanced degrees of resemblance in meaning between words, phrases, sentences, paragraphs, or even entire documents which is a challenging task for languages with low digital resources. This task becomes intricate in languages with pronoun-dropping and Subject-Object-Verb (SOV) word order specifications, such as Persian, due to their distinctive syntactic structures. One of the most important aspects of linguistic diversity lies in word order variation within languages. Some languages adhere to Subject-Object-Verb (SOV) word order, while others follow Subject-Verb-Object (SVO) patterns. These structural disparities, compounded by factors like pronoun-dropping, render the task of measuring cross-lingual STS in such languages exceptionally intricate. In the context of low-resource languages like Persian, this study proposes a customized model based on linguistic properties. Leveraging pronoun-dropping and SOV word order specifications of Persian, we introduce an innovative enhancement: a novel weighted relative positional encoding integrated into the self-attention mechanism. Moreover, we enrich context representations by infusing co-occurrence information through pointwise mutual information (PMI) factors. This paper introduces a cross-lingual model for semantic similarity analysis between Persian and English texts, utilizing parallel corpora. The experiments show that our proposed model achieves better performance than other models. Ablation study also shows that our system can converge faster and is less prone to overfitting. The proposed model is evaluated on Persian-English and Persian-Persian STS-Benchmarks and achieved 88.29% and 91.65% Pearson correlation coefficients on monolingual and cross-lingual STS-B, respectively.

In this research, the researchers seek to present a mechanism for digital governance foresight in the direction of urban smartness with a sustainability approach based on scenario writing in the city of Tehran.

The research method is mixed in terms of how to check the data; Because it uses both quantitative research strategies (in expert data) and qualitative method strategy (in interview content analysis). In terms of the nature of the data, the current research uses both quantitative and qualitative methods. This article is included in basic-applied research. Because the research is exploratory and its main purpose is to identify the environmental drivers related to the subject of the research, therefore the research is of a fundamental type; At the same time, its achievements are included as a benchmark for urban management, especially relevant organizations including the municipality, so it is also considered practical. The statistical population of the research includes elites, managers, and senior experts, whose opinions can be used in the field of digital governance and urban smartness with a sustainable approach.

Based on the results, the first scenarios in the areas of intelligence, participation, transparency, structural arrangements, integration, culture and stabilization of the best scenario and the sixth scenario, and to some extent scenario 5, the worst possible scenarios are the worst. The second to fourth scenarios are based on the least changes in the main factors and showed improvement in one factor and in one factor the regression was shown.

Resulting : 

The results showed that capacity-building to create the right to access information, increase law-abiding, discipline urban management mechanisms, and strengthen internal platforms for networking and securing information in line with urban intelligence can be implemented through the implementation of digital governance requirements.