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·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667
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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to:
• Information Sciences
• Optimization Sciences
• Control Theory
• Operational Research
• Decision Sciences
• Information Theory
• Information Technology
• Computer Networks and Communications
• Mathematical Programming
• Modelling and Simulation
• Database Management
• Applications to Engineering Sciences
• Applications to Technology
Issues up to 2022 co-published with and available at:
Hybrid machine learning models for solving complex optimization problems in information systems
*Sandip DesaiCorresponding authorsad.ycce@gmail.comDepartment of Electronics & Telecommunication Engineering Yeshwantrao Chavan College of EngineeringNagpur, Maharashtra, 441110, IndiaView full profile →
, Neha Amol Rahulrahulna@rknec.eduSchool of Management Ramdeobaba UniversityNagpur, Maharashtra, 440013, IndiaView full profile →
, Vaishali Latkevaishali.latke@pccoer.inDepartment of Computer Engineering PCET’s Pimpri Chinchwad College of Engineering and Research RavetDepartment of Computer Engineering PCET’S Pimpri Chinchwad College of Engineering and Research RavetPune, Maharashtra, 412101, IndiaView full profile →
, Trupti Deshmukhtrupti.deshmukh@dypvp.edu.inDepartment of Computer Engineering Dr. D. Y. Patil Institute of Technology Pimpri Pune, Maharashtra, 411018, IndiaView full profile →
, Parikshit Mahalleparikshit.mahalle@vit.eduDepartment of Artificial Intelligence and Data Science Vishwakarma Institute of TechnologyDepartment of Artificial Intelligence & Data Science Vishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile →
, Laxmikant Umatelaxmikantumate14@gmail.comJawaharlal Nehru Medical College Datta Meghe Institute of Higher Education and Research Wardha, Maharashtra, 442001, IndiaView full profile →
* Corresponding author · click or hover a name for details
This paper investigates the viability of crossover machine learning (ML) models in understanding complex optimization issues inside data frameworks. Crossover ML models, which combine two or more conventional machine learning strategies, are progressively utilized to handle multidimensional and energetic challenges where single-model approaches may drop brief. This think about surveys different crossover models, counting those that coordinated neural systems with developmental calculations, and their applications in regions such as supply chain administration, arrange optimization, and asset allotment. The study moreover discuss about the challenges of executing these models, such as computational requests and require for specialized information, nearby potential arrangements.
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