Panama City Fisheries Resources Office: FY 2003 Annual Report

HIGHLIGHTS FOR FY 2003 1. Continued a 3-year threatened Gulf sturgeon population estimate in the Escambia River, Florida and conducted presence-absence surveys in 4 other Florida river systems and 1 bay. 2. Five juvenile Gulf sturgeon collected, near the mouth of the Choctawhatchee River, Florida, were equipped with sonic tags and monitored while over-wintering in Choctawhatchee Bay. 3. Continued to examine Gulf sturgeon marine habitat use. 4. Implemented Gulf Striped Bass Restoration Plan by coordinating the 20th Annual Morone Workshop, leading the technical committee, transporting broodfish, and coordinating the stocking on the Apalachicola-Chattahoochee-Flint (ACF) river system. 5. Over 73,000 Phase II Gulf striped bass were marked with sequential coded wire tags and stocked in the Apalachicola River. Post-stocking evaluations were conducted at 31 sites. 6. Three stream fisheries assessment s were completed to evaluate the fish community at sites slated for habitat restoration by the Partners for Fish and Wildlife Program (PFW). 7. PFW program identified restoration needs and opportunities for 10 areas. 8. Developed an Unpaved Road Evaluation Handbook. 9. Completed restoration of Chipola River Greenway, Seibenhener Streambank Restoration, Blackwater River State Forest, and Anderson Property. 10. Assessments for fluvial geomorphic conditions for design criteria were completed for 3 projects. 11. Geomorphology in Florida streams initiated development of Rosgen regional curves for Northwest Florida for use by the Florida Department of Transportation. 12. Developed a Memorandum of Understanding between partners for enhancing, protecting, and restoring stream, wetland, and upland habitat in northwest Florida 13. Completed aquatic fauna and fish surveys with new emphasis on integration of data from reach level into watershed and landscape scale and keeping database current. 14. Compliance based sampling of impaired waterbodies on Eglin Air Force Base in conjunction with Florida Department of Environmental Protection for Total Maximum Daily Load development support. 15. Surveyed 20 sites for the federally endangered Okaloosa darter, provided habitat descriptions, worked with partners to implement key recovery tasks and set priorities for restoration. 16. Worked with partners to develop a freshwater mussel survey protocol to provide standard operating procedures for establishing the presence/absence of federally listed mussel species within a Federal project area. 17. GIS database was created to identify all known freshwater mussel records from the northeast Gulf ecosystem. 18. Completed recovery plan for seven freshwater mussels and drafted candidate elevation package for seven additional mussels. Developed proposals to implement recovery plan. 19. Worked with Corps of Engineers and State partners to develop improved reservoir operating policies to benefit both riverine and reservoir fisheries for the ACF river system. 20. Multiple outreach projects were completed to detail aquatic resources conservation opportunities. 21. Multiple stream restoration and watershed management projects initiated or completed (see Appendix A)

Towards Integration of Artificial Intelligence into Medical Devices as a Real-Time Recommender System for Personalised Healthcare:State-of-the-Art and Future Prospects

In the era of big data, artificial intelligence (AI) algorithms have the potential to revolutionize healthcare by improving patient outcomes and reducing healthcare costs. AI algorithms have frequently been used in health care for predictive modelling, image analysis and drug discovery. Moreover, as a recommender system, these algorithms have shown promising impacts on personalized healthcare provision. A recommender system learns the behaviour of the user and predicts their current preferences (recommends) based on their previous preferences. Implementing AI as a recommender system improves this prediction accuracy and solves cold start and data sparsity problems. However, most of the methods and algorithms are tested in a simulated setting which cannot recapitulate the influencing factors of the real world. This review article systematically reviews prevailing methodologies in recommender systems and discusses the AI algorithms as recommender systems specifically in the field of healthcare. It also provides discussion around the most cutting-edge academic and practical contributions present in the literature, identifies performance evaluation matrices, challenges in the implementation of AI as a recommender system, and acceptance of AI-based recommender systems by clinicians. The findings of this article direct researchers and professionals to comprehend currently developed recommender systems and the future of medical devices integrated with real-time recommender systems for personalized healthcare

A Review of Movie Recommendation System : Limitations, Survey and Challenges

Recommendation System is a major area which is very popular and useful for people to take proper decision. It is a method that helps user to find out the information which is beneficial for the user from variety of data available. When it comes to Movie Recommendation System, recommendation is done based on similarity between users (Collaborative Filtering) or by considering particular user's activity (Content Based Filtering) which he wants to engage with. So to overcome the limitations of collaborative and content based filtering generally, combination of collaborative and content based filtering is used so that a better recommendation system can be developed. Also various similarity measures are used to find out similarity between users for recommendation. In this paper, we have reviewed different similarity measures. Various companies like face book which recommends friends, LinkedIn which recommends job, Pandora recommends music, Netflix recommends movies, Amazon recommends products etc. use recommendation system to increase their profit and also benefit their customers. This paper mainly concentrates on the brief review of the different techniques and its methods for movie recommendation, so that research in recommendation system can be explored

A Survey of e-Commerce Recommender Systems

Due to their powerful personalization and efficiency features, recommendation systems are being used extensively in many online environments. Recommender systems provide great opportunities to businesses, therefore research on developing new recommender system techniques and methods have been receiving increasing attention. This paper reviews recent developments in recommender systems in the domain of ecommerce. The main purpose of the paper is to summarize and compare the latest improvements of e-commerce recommender systems from the perspective of e-vendors. By examining the recent publications in the field, our research provides thorough analysis of current advancements and attempts to identify the existing issues in recommender systems. Final outcomes give practitioners and researchers the necessary insights and directions on recommender systems

Mixed Similarity Diffusion for Recommendation on Bipartite Networks

© 2013 IEEE. In recommender systems, collaborative filtering technology is an important method to evaluate user preference through exploiting user feedback data, and has been widely used in industrial areas. Diffusion-based recommendation algorithms inspired by diffusion phenomenon in physical dynamics are a crucial branch of collaborative filtering technology, which use a bipartite network to represent collection behaviors between users and items. However, diffusion-based recommendation algorithms calculate the similarity between users and make recommendations by only considering implicit feedback but neglecting the benefits from explicit feedback data, which would be a significant feature in recommender systems. This paper proposes a mixed similarity diffusion model to integrate both explicit feedback and implicit feedback. First, cosine similarity between users is calculated by explicit feedback, and we integrate it with resource-allocation index calculated by implicit feedback. We further improve the performance of the mixed similarity diffusion model by considering the degrees of users and items at the same time in diffusion processes. Some sophisticated experiments are designed to evaluate our proposed method on three real-world data sets. Experimental results indicate that recommendations given by the mixed similarity diffusion perform better on both the accuracy and the diversity than that of most state-of-the-art algorithms