نشریه مدیریت مهندسی و رایانش نرم
سال نهم شماره 1 (پیاپی 16، بهار و تابستان 1402)

In this research, by studying the research background and using the latest version of SCOR model and experts' opinion, while identifying the factors affecting supply chain stability and determining the weight of each factor by IVHF-DANP method, the priority of SSCM rings has been determined using EHFL-VIKOR method in cement industry with the aim of improving supply chain performance. Consequently, in addition to six criteria of SCOR model and three dimensions of sustainability approach, ten other factors have been determined by experts and in total, the weight of nineteen factors has been determined and the customer's interest ring has the most preference. Then, the relevant mathematical model was presented and solved in GAMS software using NSGA-II method in MATLAB software. The combined integrated model of sustainable supply chain has been dealt with in this research since the whole supply chain -from the primary supplier to the final customer- is considered in forming and solving the model and each of the objective functions has been realized by assigning the characteristics of the SCOR model to the three dimensions of the sustainability approach formed accordingly.

Multi-objective location-routing problem is one of the most important research areas in the field of transportation and distribution management. The aim of this study is to optimize a multi-objective problem. Combining two routing and location problems, considering a set of warehouses, meeting the customer’s requirements from each warehouse, and designing an optimal route for the vehicle that brings the lowest cost to the transportation system are the main objectives of this research. Although factors such as customer satisfaction with receiving services, fuel constraints in vehicles and the existence of important time intervals, which are referred to as hard time window, are of great importance in location and routing problems, less has been paid to them. In this research, efforts have been made to address these issues. To achieve the best priority by finding the shortest route and to reach the least deviation from the time window is some of the objectives of this research. Combining variables related to vehicle fuel capacity and fuel consumption speed has also been applied in this study. In this research, first, a mixed integer linear programming model is presented and then metaheuristic method based on Non-dominated Sorting Genetic Algorithm is proposed to find the optimal solution. To evaluate the proposed performance, an example is mentioned in this framework. The result of computational experiments, shows the efficiency of the existing research methodology and its strengths and weaknesses.

Diabetes is one of the most common, dangerous and costly diseases in the world today, which is increasing at an alarming rate. The use of data mining methods can help in the early diagnosis of diabetes, which prevents the progression of this disease and many of its complications such as cardiovascular disease, vision problems and kidney disease. Providing care and health services to people with diabetes provides useful information that can be used to identify, treat, follow-up care and even prevent diabetes. In this study, a new method is presented to improve the diagnosis and prevention of diabetes using data mining methods. In this research, the DBSCAN clustering algorithm is used to cluster the data. Then, using SVM, we classify the data to identify useful data, and finally, with the firefly algorithm, we increase the obtained data to increase we optimize performance with this algorithm. The results of this study indicate that the DBSCAN algorithm is more efficient than other clustering algorithms. Also, the SVM algorithm can achieve 98% accuracy, which compared to other data mining algorithms could achieve a higher accuracy percentage.

Freezing of the gait (FOG) is a complication of Parkinson's disease (PD) that leads to the patient's inability to perform motor activities. The occurrence of FOG reduces patients' independence in daily activities and generally reduces their quality of life. The use of computational methods can provide non-pharmacological support and complementary information about the disease to neurologists by carefully examining patients’ FOG status and increasing the likelihood of a more effective treatment. This paper presents a method for FOG detection based on deep learning and signal processing techniques. The data used for this paper is the Daphnet data collection, which is collected by the wearable sensors on the patient's body. The proposed methoddetects FOG by providing a deep neural network architecture based on two-way short-term memory networks (BDL-FOG). Experimental results show that the proposed method, due to its better compatibility with time-series data, has been able to improve the FOG detection process to achieve higher accuracy than the best available methods.

In the recent years, the feature extraction as an intermediate step in the classification, has attracted the attention of researchers. In this paper, a new supervised feature extraction method is proposed using sparse component decomposition. The proposed algorithm has two steps.In the first step, the common information of the data matrix is extracted in a low rank matrix. In he second step, a linear feature extractor method such as local preservation projection one is used to extract the final features. Then, the extracted features are fed to the support vector machine classifier. To evaluate the accuracy rate of the proposed method, three datasets are used. The results show that the proposed method outperforms compared with some state of the art methods.

One of the valuable technologies that promises optimal solutions to many problems in the field of health care, etc., is the Internet of Things. Increasing the use of this technology in smart cities requires improving the IT infrastructure for the security and privacy of users. This has led to the use of blockchain technology alongside the Internet of Things. One of the applications of blockchain is its use in e-health, which is used to create a secure framework for managing patient records and to use its financial transactions to pay for services. The purpose of this article is to use one of the unique features of blockchain called micropayment to facilitate patient-related matters in e-health. This paper provides a framework for micropayments to pay for sensors and applications used to discuss patient health care in smart cities. The framework is designed to reduce the transaction fees of IoT users. It also reduces data processing, reduces the need for separate payment channels for each entity, and ultimately reduces payment time.

Minimum Variance Beamforming methods, have a weak performance in situation where error is available in covariance matrix estimation of noise and interference. The presence of the desired signal components in the estimated noise and interference vectors is of important factors of error which significantly reduces the output SINR level of the beamformer. In this paper, in order to make the beamformer robust to the incorrect estimation of the data covariance matrix, a covariance matrix reconstruction method using the orthogonal steer vectors obtained by the Gram Schmidt algorithm along with a diagonal loading is employed. Simulation results show the superiority of the proposed method in the improvement of beam pattern, angle estimation of interferences, and output SINR level, compared to the counterparts.

Gaining a competitive advantage is very important for mobile operators. Mobile value-added services are one of the innovations that operators use to diversify their business. Cross-selling is crucial for mobile operators to generate revenue and profits. Because operators will incur lower ancillary costs compared to attracting new customers. But it is not easy for them to identify potential customers who buy the services provided by operators. In this article, an attempt has been made to facilitate the cross-selling of mobile value-added services. The data used in this research is information about the past purchases of the customers of HamrahAval Company from the value-added mobile services. In the proposed solution, the infrastructure for creating cross-selling customer profiles is discussed. In this solution, after determining the optimal category of customers using their clustering, an attempt has been made to discover the rules between the services used by customers. By creating this profile, a target community can be achieved for the cross-selling of each service.

Content retrieval and knowledge management services In IoT applications, user-aware applications always contain different information and require a lot of resources to meet user demand, and this requires a high cost to implement content retrieval and management monitoring services. Is knowledge. This article presents a way to monitor knowledge management as well as to store and retrieve user information in the Internet of Things. Using the proposed method, users can transfer their data to IoT storage servers with more security than similar methods without knowing how and where to store it. The purpose of this article is to create a user-centric system for security monitoring of knowledge management in IoT applications that can be easily used. In this regard, reducing the cost of resource allocation and knowledge management by intelligently reducing the amount of data received and the use of appropriate factors to increase the quality of IoT applications have been addressed.

The issue of energy management is considered as one of the main concerns in cloud computing systems to support the rapid growth of data centers and computing Becomes. Dynamic voltage and frequency scaling strategies focus on reducing energy consumption as well as optimizing efficiency parameters. In this paper, we present an energy-conscious and reliable three-step scheduling algorithm called ERADVFS using the DVFS technique on a cloud center processor with dynamic voltage and frequency scaling capability. The goal of this algorithm is to consume the least amount of energy while ensuring reliability and limited task scheduling. In the first step, we determine the worst execution time of each task in each virtual machine of that type on each frequency level. Then in the next step, we divide the deadline for doing the work between the tasks. Finally, by assigning the DVFS technique, we assign tasks to the best virtual machine. The main idea in selecting a virtual machine is to take advantage of the extended amount of runtime to reduce the frequency of the virtual machine.

In a wireless sensor network (WSN), due to various factors such as limited power, sensor transferability, hardware failure and network problems such as packet collisions, unreliable connection and unexpected damage, the amount sensed to the header or base station is not Arrives. Therefore, data loss is very common in wireless sensor networks. Loss of measured data greatly reduces WBAN accuracy. Because WBAN deals with the vital signs of the human body, network reliability is very important. To solve this problem, missing data must be estimated. In order to predict the missing values, a model for estimating lost data based on LSTM (short-term memory) neural network is presented in this paper. This model combines five vital signs as input to predict the amount lost. The results show that sgdm-LSTM is a good way to estimate the amount lost. In addition, experimental results show that the mean square root error of the estimated value is lower than other methods. This value is 4.1495 with the best network parameters.

اISO 9000 is an international quality management standard that introduced as key criteria for assessing suppliers, a possibility for international trade, and a requirement for government bids in many countries. Moreover, implementing this standard positively affected processes and internal communication in supply chain organizations. Inventory also plays an important role in cost management in supply chains. It seems that the implementation of this standard could improve inventory indexes in the supply chains. In this paper, 39 companies in the stock market of Iran were used to assess by two indexes of inventory return and inventory to working capital to recognize the effects of ISO 9000 on the supply chain. Results indicated that ISO 9000 had no positive effect on inventory turnover and just caused some minor improvement on inventory to capital turnover in the investigated companies.