algorithms
در نشریات گروه فنی و مهندسی-
Gamification is used in various fields as persuasive technology, especially in learning and education applications. Personal gamification changes or suggests the content of games and elements based on the specific characteristics of users. The purpose of this article is to create and implement a framework in personal gamification design in the field of data science learning, which uses recommender systems algorithms for the first time to improve data quality in these algorithms. This framework utilizes implicit and explicit voting in actual time and provides a dynamic and personalized environment for the enhancement quality of understanding data science learners. In this study, we developed a game environment to learn data science and its categories. Different elements of the game were considered for the challenges that existed in the process of learning. 680 students joined this system and were divided into 8 classes. After three months of users using the system, according to the collected logs and also the comments on the personalized gamification algorithm model, it was implemented by machine algorithms and suggestions were presented to the students on the site about the elements and content. With notice to root mean square error (RMSE) and mean square error (MSE) criteria, the singular value decomposition (SVD) algorithm had better results in recommender algorithms and was used in personalized gamification. The t-test and A/B test of this framework had positive effectsKeywords: Gamification, Personalization, Recommender System, Algorithms, Learning, Data Science
-
In today's competitive market, manufacturers and service providers are continuously seeking ways to reduce costs and save time to gain a competitive edge. One of the most significant challenges they face is the vehicle routing problem (VRP), which is crucial due to its direct impact on the delivery time of services or products. Efficient vehicle routing not only enhances delivery performance but also optimizes the overall network, resulting in reduced operational costs. This study focuses on evaluating the VRP specifically for trucks while incorporating sustainability indicators into the analysis. The key sustainability indicators considered include social, economic, and environmental aspects. By integrating these indicators, the study aims to address multiple objectives simultaneously: reducing delivery time, minimizing costs, and mitigating the environmental impact of vehicle operations.The primary objective of this research is to minimize overall costs, fuel consumption, and route complexity associated with truck deliveries. Given the growing concern over environmental issues, there is a strong emphasis on improving methods to reduce greenhouse gas (GHG) emissions and streamline logistics processes. The research addresses these concerns by proposing a model that not only aims to enhance operational efficiency but also contributes to environmental protection and social responsibility.To achieve these objectives, the study employs advanced optimization techniques, specifically the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO). These methods are utilized to solve the VRP while balancing the trade-offs between various objectives, such as cost reduction, fuel efficiency, and route optimization.The results of the study indicate that the proposed model successfully improves aspects of environmental protection and social responsibility while simultaneously addressing economic concerns. The integration of sustainability indicators into the vehicle routing problem provides a comprehensive approach to optimizing logistics operations, highlighting the importance of considering environmental and social factors alongside economic performance.Overall, this research contributes to the field by offering a refined model for tackling the VRP, with a focus on sustainability. The findings underscore the potential for optimization algorithms to drive improvements in both operational efficiency and environmental stewardship, ultimately supporting more sustainable and socially responsible practices in the transportation and logistics industry.Keywords: Exchange Locations, Vehicle Routing Problem, Algorithms, Non-Dominated Sorting Genetic Algorithm-II, Multi-Objective Particle Swarm Optimization, Metaheuristic, Time Constraint
-
The sun serves as the primary energy source, providing our planet with the essential energy for sustaining life. To efficiently harness this energy, photovoltaic cells, commonly known as PV cells, are employed. These cells convert the solar energy they receive into electrical energy. The operational point of the solar cell, delivering maximum output power, is referred to as the maximum power point (MPP). However, as light availability and temperature fluctuate throughout the day, the MPP also varies accordingly. To maintain constant operation at the MPP, Maximum Power Point Tracking (MPPT) algorithms are employed to trace the MPP during module operation. These algorithms can be categorized into four groups: classical, intelligent, optimization, and hybrid, based on the tracking algorithm utilized. Each MPPT algorithm, existing in these categories, comes with its own set of advantages and limitations. This paper extensively reviews fifteen algorithms categorized under different groups. The review concludes with a comparative analysis of these algorithms, considering various parameters such as cost, complexity, tracking accuracy, and sensed parameters in a succinct manner. The paper focuses on elucidating the necessity of MPPT algorithms, their classification as per existing literature, and a comparative assessment of the studied MPPT algorithms. This comprehensive review aims to address advancements in this field, paving the way for further research.Keywords: algorithms, Maximum power point tracking, Optimization Algorithms, Photovoltaic, Power
-
Information security has become an important issue in the modern world due to its increasing popularity in Internet commerce and communication technologies such as the Internet of Things. Future media actors are considered a threat to security. Therefore, the need to use different levels of information security in different fields is more needed. Advanced information security methods are vital to prevent this type of threat. Cryptography is a valuable and efficient component for the safe transfer or storage of information in the cyber world. Familiarity with all types of encryption models is an essential need for cybersecurity experts. This paper separates Cryptographic algorithms into symmetric (SYM) and asymmetric (ASYM) categories based on the type of cryptographic structure. SYM algorithms mostly use the Feistel network (FN) structure, Substitution-Permutation Network (SPN), and the ASYM algorithms follow the mathematical structures. Based on this, we examined different encryption methods in terms of performance and detailed comparison of key size, block size, and the number of rounds. In continuation of the weakness of each algorithm against attacks and open challenges in each category, to study more is provided.
Keywords: Cryptography, Security, Algorithms -
Cloud computing is an essential tool for sharing resources across virtual machines, and it relies on scheduling and load balancing to ensure that tasks are assigned to the most appropriate resources. Multiple independent tasks need to be handled by cloud computing, and static and dynamic scheduling plays a crucial role in allocating tasks to the right resources. This is especially important in heterogeneous environments, where algorithms can improve load balancing and enhance cloud computing's efficiency. This paper aims to evaluate and discuss algorithms that can improve load balancing in cloud systems.Keywords: cloud computing, algorithms, Computer science, software engineering
-
Management information system (MIS), decision support system (DSS), and executive support system (EES) are the inevitable constituents of the intelligent systems which are being integrated with the infrastructural and technological development of the organizations to address non-routine decisions. The intelligent systems are incorporated with methodologies that support providing solutions to unpredicted decisions by employing mathematical and statistical tools and incorporating software programs embedded with cutting-edge algorithms. We investigate the applicability of several algorithms in the healthcare domain and propose mechanisms of development of machine learning techniques in the area of artificial intelligence. Artificial intelligence (AI) encompasses integer linear programming (ILP) and machine learning (ML) that further motivates us to dig up the algorithms and learning techniques to find the best solution in the field of predictive analytics for the supervised learning environments in correlating blood glucose concentration and hematocrit volume.
Keywords: Healthcare, AI, ML, Algorithms, Blood Glucose Monitoring System, Predictive Analytics, Deep Learning, Neural Network, Artificial Neural Network, Support Vector Machine -
Facility location problem (FLP) is a mathematical way to optimally locate facilities within a set of candidates to satisfy the requirements of a given set of clients. This study addressed the uncapacitated FLP as it assures that the capacity of every selected facility is finite. Thus, even if the demand is not known, which often is the case, in reality, organizations may still be able to take strategic decisions such as locating the facilities. There are different approaches relevant to the uncapacitated FLP. Here, the cuckoo search via Lévy flight (CS-LF) was used to solve the problem. Though hybrid methods produce better results, this study employed CS-LF to determine first its potential in finding solutions for the problem, particularly when applied to a real-world problem. The method was applied to the data set obtained from a department store in Davao City, Philippines. Results showed that applying CS-LF yielded better facility locations compared to particle swarm optimization and other existing algorithms. Although these results showed that CS-LF is a promising method to solve this particular problem, further studies on other FLP are recommended to establish a strong foundation of the capability of CS-LF in solving FLP.
Keywords: FLP, CS- FLP, Optimization problem, Metaheuristics, Hybrid, Algorithms -
The purpose of this research, is to study factors influencing privacy concerns about data security and protection on social network sites and its influence on self-disclosure. 100 articles about privacy protection, data security, information disclosure and Information leakage on social networks were studied. Models and algorithms types and their repetition in articles have been distinguished and this study builds a research model to examine privacy concerns and the effect of it on self-disclosure. The need of having knowledge and skill about privacy protection seems to be necessary with social networks technology developments. Most of the researches have been studied about privacy protection scope related to users privacy on social networks including women, men, children and adults in smartphone and E-health. Most of researches on this scope have been done in USA. Most studies were focused on privacy protection and security on social networks.Keywords: Privacy Protection, Social Networks, Information Leakage. Information Disclosure, Tools, Algorithms
-
در این مقاله به بررسی الگوریتم شناسایی موانع پیرامون خودرو هوشمند و چگونگی هدایت آن روی جاده پرداخته می شود. برای این منظور جاده در طول و عرض به تعدادی سلول تقسیم بندی می شود. فرض بر این است که نقاط اشغال شده سلولها توسط وسایل خاصی مشخص می شود و یک ماتریس متناظر خانه های پر و خالی جاده تولید می-شود. در این ماتریس، سلول های پر با عدد یک و سلول های خالی با عدد صفر نشان داده شده اند. در مرحله ی بعد با تحلیل ماتریس به دست آمده در نرم افزار متلب خودرو هدایت می شود. در این تحلیل ابتدا موقعیت خودرو و موانع مشخص می شود. سپس با توجه به شرایط جاده و موقعیت موانع، دستورات لازم برای هدایت خودرو تعیین می شود. در صورت نیاز به تغییر خط، با توجه به انحنای جاده و فاصله ی خودرو تا مانع، مسیر مناسب برای حرکت خودرو انتخاب می شود. در این مقاله برای اولین بار در راه هدایت خودرو هوشمند، جاده به عنوان یک ماتریس صفر و یک در نظر گرفته شده است. در این روش ماتریس جاده با گذشت زمان به روز رسانی می شود و امکان تحلیل نوع حرکت خودرو از میان موانع را فراهم می سازد. همچنین الگوریتم استفاده شده در حل مساله بسیار ساده می باشد.کلید واژگان: الگوریتم، تقسیم بندی سلولی، ماتریس جاده، تغییر خطIn this paper, the algorithm to detect obstacles surrounding an autonomous vehicle and the method to navigate this vehicle on the road are studied. For this purpose, the road is divided into cells in lateral and longitudinal directions. The assumption is that some special tools specify the cells positions and then full and empty-cell corresponding matrix is generated. In this matrix, full cells are displayed with digit 1 and empty cells are displayed with digit 0. In the next step, by analyzing the matrix in Matlab, the vehicle is navigated. In this analysis, firstly the position of the vehicle and the obstacles are identified. Then, based on the road conditions and the obstacles positions, required orders to move the vehicle are determined. If a lane change is needed, according to the roads curvature and the distance between the vehicle and the obstacle, appropriate path for the vehicle will be chosen. In this paper, for the first time in autonomous vehicle navigations, the road is considered as a 1 and 0 matrix. In this method, the road matrix gets updated with time and provides the possibility of analyzing the vehicles movement. Also, the algorithm used to solve the problem is very simple.Keywords: Algorithms, Cell Decomposition, Road Matrix, Lane Change
- نتایج بر اساس تاریخ انتشار مرتب شدهاند.
- کلیدواژه مورد نظر شما تنها در فیلد کلیدواژگان مقالات جستجو شدهاست. به منظور حذف نتایج غیر مرتبط، جستجو تنها در مقالات مجلاتی انجام شده که با مجله ماخذ هم موضوع هستند.
- در صورتی که میخواهید جستجو را در همه موضوعات و با شرایط دیگر تکرار کنید به صفحه جستجوی پیشرفته مجلات مراجعه کنید.