International Journal of Advanced Intelligence Paradigms
- Editors in Chief
- Prof. Valentina E. Balas, Prof. Anca Ralescu
- ISSN online
- ISSN print
- 12 issues per year
- CiteScore 1.1 (2021)
IJAIP fosters the exchange and dissemination of applications and case studies in the area of advanced intelligence paradigms among education and research professionals. The thrust of the journal is to publish papers dealing with the design, development, testing, implementation and management of advanced intelligent systems, and to provide guidelines in the development/management of these systems. IJAIP publishes archival articles and assessments of current trends, providing a medium for exchanging scientific research and technological achievements accomplished by the international community.
IJAIP is an Open Access-only journal and article processing charges (APCs) apply.
Topics covered include
- Advanced machine learning paradigms
- Soft computing
- Possibility theory
- Probabilistic reasoning
- Belief functions
- Rough sets
- Decision theory
- Philosophical foundation
- Psychological models
IJAIP is intended to serve as a forum for the communications of original research of advanced intelligence paradigms for evolving advanced intelligent systems for scientific applications.
ReadershipThe audience of IJAIP consists of researchers, application engineers, scientists, decision-makers and graduate students in computing science, automatics, engineering, management, health sciences, avionics, applied mathematics and related disciplines.
IJAIP publishes original articles on current and potential paradigms and applications. Conferences reports, book reviews, notes, commentaries and news will continue to provide information on the latest trends and developments in this ever expanding area. Special Issues devoted to the relevant topics will also be published.
IJAIP is indexed in:More indexes...
- DBLP Computer Science Bibliography
- Expanded Academic ASAP (Gale)
- Google Scholar
- Info Trac (Gale)
- Inspec (Institution of Engineering and Technology)
IJAIP is listed in:
- National Agency for Evaluation of the University and Research System (ANVUR)
- Cabell's Directory of Publishing Opportunities
- Pedrycz, Witold, University of Alberta, Canada
Editors in Chief
- Balas, Valentina E., Aurel Vlaicu University of Arad, Romania
- Ralescu, Anca, University of Cincinnati, USA
Editorial Advisory Board
- Abe, Jair Minoro, Paulista University, Brazil
- Castillo, Oscar, Tijuana Institute of Technology, Mexico
- Kacprzyk, Janusz, Polish Academy of Sciences, Poland
- Koczy, Laszlo T., Budapest University of Technology and Economics, Hungary
- Negoita, Constantin V., City University of New York, USA
- Ralescu, Dan, University of Cincinnati, USA
- Sugeno, Michio, Doshisha University, Japan
- Teodorescu, Horia-Nicolai, Technical University, Romania
Editorial Board Members
- Abeynayake, Canicious, Defence Science and Technology Organisation, Australia
- Aghababa, Mohammad Pourmahmood, Urmia University of Technology, Iran
- Arif, Muhammad, University of Gujrat, Pakistan
- Azar, Ahmad Taher, Prince Sultan University, Kingdom of Saudi Arabia and Benha University, Egypt
- Balachandran, Bala M., University of Canberra, Australia
- Balas, Marius M., Aurel Vlaicu University of Arad, Romania
- Banerjee, Soumya, Birla Institute of Technology Mesra Extension Centre, Deoghar, India
- Bannore, Vivek, ThinkingSpace IT Solutions, Australia
- Borumand Saeid, Arsham, Shahid Bahonar University of Kerman, Iran
- Chung, Sheng-Luen, National Taiwan University of Science & Technology, Taiwan, Province of China
- Ciugudean, Mircea, Politehnica University of Timisoara, Romania
- Cococcioni, Marco, University of Pisa, Italy
- Deep, Kusum, Indian Institute of Technology Roorkee, India
- Dey, Rajeeb, National Institute of Technology, Silchar, India
- Djurovic, Igor, University of Montenegro, Montenegro
- Dogaru, Radu, Polytechnic University of Bucharest, Romania
- Dombi, Jozsef, University of Szeged, Hungary
- Dzitac, Simona, University of Oradea, Romania
- Foulloy, Laurent, Université de Savoie, France
- Groza, Voicu, University of Ottawa, Canada
- Hanachi, Chihab, University Toulouse 1 Capitole, France
- Hemanth, D. Jude, Karunya University, India
- Ichalkaranje, Nikhil, Australian Government Department of Broadband, Communications, and the Digital Economy, Australia
- Kuo, Chung-Hsien, National Taiwan University of Science and Technology, Taiwan, Province of China
- Kwasnicka, Halina, Wroclaw University of Technology, Poland
- Li, Xiang, Beijing Jiaotong University, China
- Lim, Chee-Peng, Deakin University, Australia
- Lin, Tsung-Chih, Feng-Chia University, Taiwan, Province of China
- Luca, Mihaela, Romanian Academy, Romania
- McCauley-Bush, Pamela, University of Central Florida, USA
- Monekosso, Dorothy N., Kingston University, UK
- Monett Díaz, Dagmar, Berlin School of Economics and Law, Germany
- Nakamatsu, Kazumi, IRNet International Academic Communication Center and Hokkaido Hair Dressing and Beauty Welfare Association, Japan
- Nicolau, Viorel, Dunarea de Jos University of Galati, Romania
- Pavone, Mario, University of Catania, Italy
- Petriu, Emil M., University of Ottawa, Canada
- Pintea, Camelia, Technical University of Cluj Napoca, Romania
- Popescu, Daniela E., University of Oradea, Romania
- Popescu, Dumitru, Politehnica University of Bucharest, Romania
- Popescu-Bodorin, Nicolaie, University of South-East Europe, Bucharest , Romania
- Pratihar, D.K., Indian Institute of Technology, Kharagpur, India
- Precup, Radu Emil, Politehnica University of Timisoara, Romania
- Prostean, Octavian, Politehnica University of Timisoara, Romania
- Rao, D. Vijay, Defence Research and Development Organisation (DRDO), India
- Rasheed, Haroon, Bahria University, Pakistan
- Roy, Sanjiban Sekhar, VIT University, India
- Rudas, Imre, Budapest Tech Polytechnical Institution, Hungary
- Sarkar, Joy Lal, Amrita Vishwa Vidyapetham, India
- Satapathy, Suresh Chandra, Kalinga Institute of Industrial Technology, India
- Shahbazova, Shahnaz N., Azerbaijan Technical University, Azerbaijan
- Sharma, Dharmendra, University of Canberra, Australia
- Tan, Hong-Zhou, Sun Yat-Sen University, China
- Toshniwal, Durga, Indian Institute of Technology Roorkee, India
- Tweedale, Jeff, Defence Science and Technology Organisation, Australia
- Varkonyi-Koczy, Annamaria R., Budapest University of Technology and Economics, Hungary
- Zhang, Zili, Deakin University, Australia
A few essentials for publishing in this journal
- Submitted articles should not have been previously published or be currently under consideration for publication elsewhere.
- Conference papers may only be submitted if the paper has been completely re-written (more details available here) and the author has cleared any necessary permissions with the copyright owner if it has been previously copyrighted.
- Briefs and research notes are not published in this journal.
- All our articles go through a double-blind review process.
- All authors must declare they have read and agreed to the content of the submitted article. A full statement of our Ethical Guidelines for Authors (PDF) is available.
- This is an Open Access-only journal. There is an article processing charge of US $1600 to publish in this journal. You can find more information on OA here.
- In May 2019, IJAIP became an Open Access-only journal and articles incur an article processing charge to be published. If your article was submitted before May 2019, you can still get your accepted article published with a standard licence, free of charge.
- All articles for this journal must be submitted using our online submissions system.
- View Author guidelines.
Bagging and boosting your way to a spam-free inbox
11 January, 2023
Research in the International Journal of Advanced Intelligence Paradigms, discusses the potential of bagging and boosting of machine learning classifiers for the accurate detection of email spam. Bagging and boosting are two popular methods used to improve the performance of machine learning classifiers. They are used to improve the output from machine learning algorithms, such as decision trees, logistic regression tools, and support vector machines. Bagging, or bootstrap aggregating, is a technique used to reduce variance of results given by a machine learning model. The approach works by training several models independently on different random subsets of the training data. The predictions from those models are then averaged, this effectively smooths out the different mistakes made by each individual model so that the overall degree of error in the final output is lower than it would be for any single model [...]More details...