14 research outputs found
Intelligent Transportation Related Complex Systems and Sensors
Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data
Data Science and Knowledge Discovery
Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining
Collected Papers (on Neutrosophic Theory and Applications), Volume VI
This sixth volume of Collected Papers includes 74 papers comprising 974 pages on (theoretic and applied) neutrosophics, written between 2015-2021 by the author alone or in collaboration with the following 121 co-authors from 19 countries: Mohamed Abdel-Basset, Abdel Nasser H. Zaied, Abduallah Gamal, Amir Abdullah, Firoz Ahmad, Nadeem Ahmad, Ahmad Yusuf Adhami, Ahmed Aboelfetouh, Ahmed Mostafa Khalil, Shariful Alam, W. Alharbi, Ali Hassan, Mumtaz Ali, Amira S. Ashour, Asmaa Atef, Assia Bakali, Ayoub Bahnasse, A. A. Azzam, Willem K.M. Brauers, Bui Cong Cuong, Fausto Cavallaro, Ahmet Çevik, Robby I. Chandra, Kalaivani Chandran, Victor Chang, Chang Su Kim, Jyotir Moy Chatterjee, Victor Christianto, Chunxin Bo, Mihaela Colhon, Shyamal Dalapati, Arindam Dey, Dunqian Cao, Fahad Alsharari, Faruk Karaaslan, Aleksandra Fedajev, Daniela Gîfu, Hina Gulzar, Haitham A. El-Ghareeb, Masooma Raza Hashmi, Hewayda El-Ghawalby, Hoang Viet Long, Le Hoang Son, F. Nirmala Irudayam, Branislav Ivanov, S. Jafari, Jeong Gon Lee, Milena Jevtić, Sudan Jha, Junhui Kim, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Darjan Karabašević, Songül Karabatak, Abdullah Kargın, M. Karthika, Ieva Meidute-Kavaliauskiene, Madad Khan, Majid Khan, Manju Khari, Kifayat Ullah, K. Kishore, Kul Hur, Santanu Kumar Patro, Prem Kumar Singh, Raghvendra Kumar, Tapan Kumar Roy, Malayalan Lathamaheswari, Luu Quoc Dat, T. Madhumathi, Tahir Mahmood, Mladjan Maksimovic, Gunasekaran Manogaran, Nivetha Martin, M. Kasi Mayan, Mai Mohamed, Mohamed Talea, Muhammad Akram, Muhammad Gulistan, Raja Muhammad Hashim, Muhammad Riaz, Muhammad Saeed, Rana Muhammad Zulqarnain, Nada A. Nabeeh, Deivanayagampillai Nagarajan, Xenia Negrea, Nguyen Xuan Thao, Jagan M. Obbineni, Angelo de Oliveira, M. Parimala, Gabrijela Popovic, Ishaani Priyadarshini, Yaser Saber, Mehmet Șahin, Said Broumi, A. A. Salama, M. Saleh, Ganeshsree Selvachandran, Dönüș Șengür, Shio Gai Quek, Songtao Shao, Dragiša Stanujkić, Surapati Pramanik, Swathi Sundari Sundaramoorthy, Mirela Teodorescu, Selçuk Topal, Muhammed Turhan, Alptekin Ulutaș, Luige Vlădăreanu, Victor Vlădăreanu, Ştefan Vlăduţescu, Dan Valeriu Voinea, Volkan Duran, Navneet Yadav, Yanhui Guo, Naveed Yaqoob, Yongquan Zhou, Young Bae Jun, Xiaohong Zhang, Xiao Long Xin, Edmundas Kazimieras Zavadskas
Trustworthiness in Mobile Cyber Physical Systems
Computing and communication capabilities are increasingly embedded in diverse objects and structures in the physical environment. They will link the ‘cyberworld’ of computing and communications with the physical world. These applications are called cyber physical systems (CPS). Obviously, the increased involvement of real-world entities leads to a greater demand for trustworthy systems. Hence, we use "system trustworthiness" here, which can guarantee continuous service in the presence of internal errors or external attacks. Mobile CPS (MCPS) is a prominent subcategory of CPS in which the physical component has no permanent location. Mobile Internet devices already provide ubiquitous platforms for building novel MCPS applications. The objective of this Special Issue is to contribute to research in modern/future trustworthy MCPS, including design, modeling, simulation, dependability, and so on. It is imperative to address the issues which are critical to their mobility, report significant advances in the underlying science, and discuss the challenges of development and implementation in various applications of MCPS
Enhanced Living Environments
This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area
Grafovi preferencije i njihova primjena
Usporedba objekata (alternativa) u parovima često se koristi u kontekstu odlučivanja. Metoda potencijala koristi (težinski) graf preferencije kao osnovnu strukturu generiranu takvim usporedbama. Iz toga grafa, rješavanjem sustava jednadžbi koji uključuje njegovu Laplaceovu matricu, dobiva se funkcija vrijednosti na skupu alternativa koju nazivamo potencijalom. U višekriterijskom ili grupnom odlučivanju (npr. izbornim procedurama), svaki kriterij ili sudionik može se predstaviti grafom preferencije. Multigraf dobiven spajanjem tih grafova koristi se za agregaciju preferencija i generira grupni potencijal. Moguće je postaviti proizvoljne težine da bismo podesili utjecaj pojedinog kriterija ili sudionika na grupni potencijal. Agregaciju grafova preferencije primjenjujemo na izborne procedure. Različiti oblici glasačkih listića generiraju grafove preferencije: tako dobivamo univerzalnu izbornu proceduru koja ne ovisi o obliku glasačkog listića. Još jedna primjena je klasterska analiza skupine na temelju preferencija njezinih članova, gdje valja provesti hijerarhijsku ili particijsku klasterizaciju više grafova preferencije. U tom kontekstu, agregacijski multigraf koristimo za definiranje središta klastera ili udaljenosti dvaju klastera. Opisanu izbornu proceduru, kao i klastersku analizu, za ilustraciju smo primijenili na glasačke preferencije zemalja s Eurosong natjecanja.Pairwise comparison of various objects (alternatives) is common in many procedures related to decision making. Potential Method (PM) uses a (weighted) preference graph as the basic structure generated by such comparisons. This graph implies a value function (called potential) on the set of alternatives. The potential is calculated as a solution of a system of equations involving the Laplacian matrix of the preference graph. In multiple-criteria decision analysis or group decision making (i.e., voting systems), each criterion or decision maker is represented by a preference graph. A multigraph obtained by joining these graphs is used for preference aggregation, generating the group potential. We can set arbitrary weights to adjust the influence of each criterion or decision maker on the group potential. Aggregation of preference graphs is applied to voting systems. Many different forms of voting ballots can generate preference graphs, which gives us a universal (ballot-independent) voting system. Another application is a cluster analysis of the group based on the members’ preferences, where a hierarchical or partitional clustering of their (multiple) preference graphs should be performed. In this context, the multigraph-based aggregation is used to define the center of the cluster or the distance between two clusters. As an illustration, we have applied the described voting system, as well as the cluster analysis, to the voting data from the Eurovision Song Contest
Grafovi preferencije i njihova primjena
Usporedba objekata (alternativa) u parovima često se koristi u kontekstu odlučivanja. Metoda potencijala koristi (težinski) graf preferencije kao osnovnu strukturu generiranu takvim usporedbama. Iz toga grafa, rješavanjem sustava jednadžbi koji uključuje njegovu Laplaceovu matricu, dobiva se funkcija vrijednosti na skupu alternativa koju nazivamo potencijalom. U višekriterijskom ili grupnom odlučivanju (npr. izbornim procedurama), svaki kriterij ili sudionik može se predstaviti grafom preferencije. Multigraf dobiven spajanjem tih grafova koristi se za agregaciju preferencija i generira grupni potencijal. Moguće je postaviti proizvoljne težine da bismo podesili utjecaj pojedinog kriterija ili sudionika na grupni potencijal. Agregaciju grafova preferencije primjenjujemo na izborne procedure. Različiti oblici glasačkih listića generiraju grafove preferencije: tako dobivamo univerzalnu izbornu proceduru koja ne ovisi o obliku glasačkog listića. Još jedna primjena je klasterska analiza skupine na temelju preferencija njezinih članova, gdje valja provesti hijerarhijsku ili particijsku klasterizaciju više grafova preferencije. U tom kontekstu, agregacijski multigraf koristimo za definiranje središta klastera ili udaljenosti dvaju klastera. Opisanu izbornu proceduru, kao i klastersku analizu, za ilustraciju smo primijenili na glasačke preferencije zemalja s Eurosong natjecanja.Pairwise comparison of various objects (alternatives) is common in many procedures related to decision making. Potential Method (PM) uses a (weighted) preference graph as the basic structure generated by such comparisons. This graph implies a value function (called potential) on the set of alternatives. The potential is calculated as a solution of a system of equations involving the Laplacian matrix of the preference graph. In multiple-criteria decision analysis or group decision making (i.e., voting systems), each criterion or decision maker is represented by a preference graph. A multigraph obtained by joining these graphs is used for preference aggregation, generating the group potential. We can set arbitrary weights to adjust the influence of each criterion or decision maker on the group potential. Aggregation of preference graphs is applied to voting systems. Many different forms of voting ballots can generate preference graphs, which gives us a universal (ballot-independent) voting system. Another application is a cluster analysis of the group based on the members’ preferences, where a hierarchical or partitional clustering of their (multiple) preference graphs should be performed. In this context, the multigraph-based aggregation is used to define the center of the cluster or the distance between two clusters. As an illustration, we have applied the described voting system, as well as the cluster analysis, to the voting data from the Eurovision Song Contest
Grafovi preferencije i njihova primjena
Usporedba objekata (alternativa) u parovima često se koristi u kontekstu odlučivanja. Metoda potencijala koristi (težinski) graf preferencije kao osnovnu strukturu generiranu takvim usporedbama. Iz toga grafa, rješavanjem sustava jednadžbi koji uključuje njegovu Laplaceovu matricu, dobiva se funkcija vrijednosti na skupu alternativa koju nazivamo potencijalom. U višekriterijskom ili grupnom odlučivanju (npr. izbornim procedurama), svaki kriterij ili sudionik može se predstaviti grafom preferencije. Multigraf dobiven spajanjem tih grafova koristi se za agregaciju preferencija i generira grupni potencijal. Moguće je postaviti proizvoljne težine da bismo podesili utjecaj pojedinog kriterija ili sudionika na grupni potencijal. Agregaciju grafova preferencije primjenjujemo na izborne procedure. Različiti oblici glasačkih listića generiraju grafove preferencije: tako dobivamo univerzalnu izbornu proceduru koja ne ovisi o obliku glasačkog listića. Još jedna primjena je klasterska analiza skupine na temelju preferencija njezinih članova, gdje valja provesti hijerarhijsku ili particijsku klasterizaciju više grafova preferencije. U tom kontekstu, agregacijski multigraf koristimo za definiranje središta klastera ili udaljenosti dvaju klastera. Opisanu izbornu proceduru, kao i klastersku analizu, za ilustraciju smo primijenili na glasačke preferencije zemalja s Eurosong natjecanja.Pairwise comparison of various objects (alternatives) is common in many procedures related to decision making. Potential Method (PM) uses a (weighted) preference graph as the basic structure generated by such comparisons. This graph implies a value function (called potential) on the set of alternatives. The potential is calculated as a solution of a system of equations involving the Laplacian matrix of the preference graph. In multiple-criteria decision analysis or group decision making (i.e., voting systems), each criterion or decision maker is represented by a preference graph. A multigraph obtained by joining these graphs is used for preference aggregation, generating the group potential. We can set arbitrary weights to adjust the influence of each criterion or decision maker on the group potential. Aggregation of preference graphs is applied to voting systems. Many different forms of voting ballots can generate preference graphs, which gives us a universal (ballot-independent) voting system. Another application is a cluster analysis of the group based on the members’ preferences, where a hierarchical or partitional clustering of their (multiple) preference graphs should be performed. In this context, the multigraph-based aggregation is used to define the center of the cluster or the distance between two clusters. As an illustration, we have applied the described voting system, as well as the cluster analysis, to the voting data from the Eurovision Song Contest
Methodological guidelines for reusing general ontologies
Currently, there is a great deal of well-founded explicit knowledge formalizing general notions, such as time concepts and the part_of relation. Yet, it is often the case that instead of reusing ontologies that implement such notions (the so-called general ontologies), engineers create procedural programs that implicitly implement this knowledge. They do not save time and code by reusing explicit knowledge, and devote effort to solve problems that other people have already adequately solved. Consequently, we have developed a methodology that helps engineers to: (a) identify the type of general ontology to be reused; (b) find out which axioms and definitions should be reused; (c) make a decision, using formal concept analysis, on what general ontology is going to be reused; and (d) adapt and integrate the selected general ontology in the domain ontology to be developed. To illustrate our approach we have employed use-cases. For each use case, we provide a set of heuristics with examples. Each of these heuristics has been tested in either OWL or Prolog. Our methodology has been applied to develop a pharmaceutical product ontology. Additionally, we have carried out a controlled experiment with graduated students doing a MCs in Artificial Intelligence. This experiment has yielded some interesting findings concerning what kind of features the future extensions of the methodology should have