zahra zojaji

The pharmaceutical industry has seen increased drug production by different manufacturers. Failure to recognize future needs has caused improper production and distribution of drugs throughout the supply chain of this industry. Forecasting demand is one of the basic requirements to overcome these challenges. Forecasting the demand helps the drug to be well estimated and produced at a certain time.

Artificial intelligence (AI) technologies are suitable methods for forecasting demand. The more accurate this forecast is the better it will be to decide on the management of drug production and distribution. Isfahan AI competitions‑2023 have organized a challenge to provide models for accurately predicting drug demand. In this article, we introduce this challenge and describe the proposed approaches that led to the most successful results.

A dataset of drug sales was collected in 12 pharmacies of Hamadan University of Medical Sciences. This dataset contains 8 features, including sales amount and date of purchase. Competitors compete based on this dataset to accurately forecast the volume of demand. The purpose of this challenge is to provide a model with a minimum error rate while addressing some qualitative scientific metrics.

In this competition, methods based on AI were investigated. The results showed that machine learning methods are particularly useful in drug demand forecasting. Furthermore, changing the dimensions of the data features by adding the geographic features helps increase the accuracy of models. 

Computer-aided diagnosis (CAD) methods have become of great interest for diagnosing macular diseases over the past few decades. Artificial intelligence (AI)-based CADs offer several benefits, including speed, objectivity, and thoroughness. They are utilized as an assistance system in various ways, such as highlighting relevant disease indicators to doctors, providing diagnosis suggestions, and presenting similar past cases for comparison.

Much specifically, retinal AI-CADs have been developed to assist ophthalmologists in analyzing optical coherence tomography (OCT) images and making retinal diagnostics simpler and more accurate than before. Retinal AICAD technology could provide a new insight for the health care of humans who do not have access to a specialist doctor. AI-based classification methods are critical tools in developing improved retinal AI-CAD technology. The Isfahan AI-2023 challenge has organized a competition to provide objective formal evaluations of alternative tools in this area. In this study, we describe the challenge and those methods that had the most successful algorithms.

A dataset of OCT images, acquired from normal subjects, patients with diabetic macular edema, and patients with other macular disorders, was provided in a documented format. The dataset, including the labeled training set and unlabeled test set, was made accessible to the participants. The aim of this challenge was to maximize the performance measures for the test labels. Researchers tested their algorithms and competed for the best classification results.

The competition is organized to evaluate the current AIbased classification methods in macular pathology detection. We received several submissions to our posted datasets that indicate the growing interest in AI-CAD technology. The results demonstrated that deep learning-based methods can learn essential features of pathologic images, but much care has to be taken in choosing and adapting appropriate models for imbalanced small datasets. 

Gastroesophageal reflux disease (GERD) is a prevalent digestive disorder that impacts millions of individuals globally. Multichannel intraluminal impedance‑pH (MII‑pH) monitoring represents a novel technique and currently stands as the gold standard for diagnosing GERD. Accurately characterizing reflux events from MII data are crucial for GERD diagnosis. Despite the initial introduction of clinical literature toward software advancements several years ago, the reliable extraction of reflux events from MII data continues to pose a significant challenge. Achieving success necessitates the seamless collaboration of two key components: a reflux definition criteria protocol established by gastrointestinal experts and a comprehensive analysis of MII data for reflux detection.

In an endeavor to address this challenge, our team assembled a dataset comprising 201 MII episodes. We meticulously crafted precise reflux episode definition criteria, establishing the gold standard and labels for MII data.

A variety of signal‑analyzing methods should be explored. The first Isfahan Artificial Intelligence Competition in 2023 featured formal assessments of alternative methodologies across six distinct domains, including MII data evaluations.

This article outlines the datasets provided to participants and offers an overview of the competition results. 

Multiple sclerosis (MS) is one of the most common reasons of neurological disabilities in young adults. The disease occurs when the immune system attacks the central nervous system and destroys the myelin of nervous cells. This results in appearing several lesions in the magnetic resonance (MR) images of patients. Accurate determination of the amount and the place of lesions can help physicians to determine the severity and progress of the disease.

Due to the importance of this issue, this challenge has been dedicated to the segmentation and localization of lesions in MR images of patients with MS. The goal was to segment and localize the lesions in the flair MR images of patients as close as possible to the ground truth masks.

Several teams sent us their results for the segmentation and localization of lesions in MR images. Most of the teams preferred to use deep learning methods. The methods varied from a simple U-net structure to more complicated networks.

The results show that deep learning methods can be useful for segmentation and localization of lesions in MR images. In this study, we briefly described the dataset and the methods of teams attending the competition.

Social networks have become a central part of our lives these days and have real effects on the world's events. However, social networks greatly boost spreading misinformation and rumors that are becoming more and more dangerous each day. As fighting rumors first requires detecting them, several researchers tried to propose novel approaches for automatic early detection of rumors. However, most of them rely on handcrafted content features which makes them prone to deception and threats the adaptability of the model. Furthermore, a great deal of work have concentrated on event-level rumor detection while it faces early detection with serious challenges. There are also deficiencies in proposed methods in terms of time and resource complexity. This study proposes a deep learning approach to automate the detection of rumors on Twitter. The proposed method relies on automatically extracted features through word and sentence embeddings along with profile and network-based features. It then uses Recurrent Neural Networks (RNN) leveraging Gated Recurrent Units (GRU) for detecting the veracity of a tweet. The proposed method also improves time efficiency. The achieved experimental evaluation results on RumorEval2019 dataset demonstrate that the proposed method outperforms other rival models on the same dataset in terms of both performance and time complexity. By the way, the proposed method is more resilient to deception by avoiding the use of handcrafted content features and leveraging features that are out of the control of the user.

Nowadays, the tourism industry has become one of the most important sectors in the world economy. Due to the perishability of this industry, accurate forecasting of the demand is very important for tourism planning and resource allocation. Studies show that due to the diversity and complexity of the factors affecting tourism demand, the combination of different approaches may increase the forecasting accuracy. The aim of this paper is to forecast the tourism demand of Alisadr cave. For this purpose, a method based on artificial neural networks is presented, in which the results of linear and non-linear methods and short-term and long-term forecasts are combined. This method is applied to a dataset of Alisadr cave tourists. The evaluation results show that in most cases, the proposed combined method can predict the tourism demand with higher accuracy than the monthly and seasonal methods based on neural networks and random forest models. The predictive models obtained from this study can enhance customer service and improve the interaction between users and tourist ticketing web applications and online reservation programs.

The role of genetic components in expression of proteins involved in signaling pathways of fat and carbohydrate metabolism has been well-demonstrated. The aim of this study was to determine effects of high intensity interval training (HIIT) on glucose, insulin, and insulin resistance levels as well as IRS1 expression in gastrocnemius muscle of obese Wistar rats.

The study included 14 male, Wistar rats (aged 10 weeks) weighting 220 ± 20 g. Obesity was induced in all rats via exposure to a high-fat diet for six weeks. Then, the rats were randomly divided into a HIIT group (n=7) and a control group (n=7). The rats in the HIIT group performed treadmill running, five sessions a week, for eight weeks. Levels of fasting glucose, serum insulin, insulin resistance, and IRS1 expression in the gastrocnemius muscle of the rats were measured after the last training session. Data were analyzed by the independent t-test at statistical significance of 0.05.

The HIIT intervention significantly decreased fasting glucose compared with the control group (p<0.0001). It also resulted in a significant decrease in serum insulin levels and insulin resistance compared with the control group (p<0.0001). Moreover, the HIIT training significantly increased IRS1 expression (p=0.030) in the gastrocnemius muscle of rats.

Based on the available evidence, the increase in insulin function and the decrease in insulin resistance can be attributed to increased IRS1 expression in the gastrocnemius muscle following HIIT training.

Combinatorial optimization is the procedure of optimizing an objective function over the discrete configuration space. A genetic algorithm (GA) has been applied successfully to solve various NP-complete combinatorial optimization problems. One of the most challenging problems in applying GA is selecting mutation operators and associated probabilities for each situation. GA uses just one type of mutation operator with a specified probability in the basic form. The mutation operator is often selected randomly in improved GAs that leverage several mutation operators. While an effective GA search occurs when the mutation type for each chromosome is selected according to mutant genes and the problem landscape. This paper proposes an adaptive genetic algorithm that uses Q-learning to learn the best mutation strategy for each chromosome. In the proposed method, the success history of the mutant in solving the problem is utilized for specifying the best mutation type. For evaluating adaptive genetic algorithm, we adopted the traveling salesman problem (TSP) as a well-known problem in the field of optimization. The results of the adaptive genetic algorithm on five datasets show that this algorithm performs better than single mutation GAs up to 14% for average cases. It is also indicated that the proposed algorithm converges faster than single mutation GAs.

In this paper, the impact of different operative variables on the quality of cutting of Inconel material 718 is studied. Utilizing Taguchi test design, the input variables including carbon dioxide laser power and the cutting speed for cutting three different thicknesses of Inconel 718 alloy were investigated in order to achieve the optimal conditions. After obtaining experimental test results, dataset was modeled using artificial neural networks. The neural network model is then used for evaluating candidate solutions in particle swarm optimization (PSO) algorithm which is employed for optimization of cutting conditions. The results indicated that when the laser power of is 1714 (W), the cutting speed is 1382 (mm/min) and the thickness of the material is 0.8 (mm), The best quality for cutting Inconel 718 is achieved with a carbon dioxide laser cutting machine. The results of optimal cutting parameters of Inconel alloy with carbon dioxide laser which were obtained by PSO were verified through an experimental test and similar papers. The results of this experimental test were very close to the optimal values of the PSO, which demonstrates the efficiency of neural network model in predicting the quality of cutting and the efficiency of PSO in finding optimal conditions.

The use of lasers is being considered as a modern method for forming process in recent years. This method has been used in various industries, such as aerospace, marine and oil industry. Extensive research has been done in the field of modeling and optimization of direct paths parameters with process of laser forming. Although forming in circular paths can be used for producing complex parts, due to some technical reasons, it is considered less. The main purpose of this paper is to detect the proper estimation model and obtain optimal variables conditions for complete circular paths in perforated circular parts by means of genetic algorithms. In this process the outer edges are fixed and the inner edges are being formed by laser. At first, the finite element simulation model is studied then the estimation model has been discussed, after that multi-objective functions have been examined with the least error and energy. Furthermore, the optimization results of the internal hole diameters are reported and analyzed in terms of Pareto charts. In conclusion, optimum forming conditions have been reported in terms of accuracy and energy for different diameters of holes. This study shows with acceptable increasing in the error rate, the required energy could be reduced. Also, increasing in the diameter of inside hole cause to increase energy and decrease of accuracy.