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london,
united kingdom
Aktualisht është 8:59 p.d. këtu
U bashkua shkurt 22, 2024
0 Rekomanadimet
Mohammad B.
@mb320
1,1
1,1
100%
100%
london,
united kingdom
100%
Punët e Plotësuara
Asnjë
Brenda Buxhetit
Asnjë
Në Kohë
Asnjë
Shpeshtësia e Rimarrjes në Punë
senior Software Developer
Kontaktoni Mohammad B. për punën tuaj
Identifikohu për të diskutuar çdo detaj gjatë bisedës.
Vlerësimet
Ndryshimet u ruajtën
Asnjë vlerësim këtu!
Përvoja
Senior Software Developer
maj 2020 - E tashme
Intranet applications:
-Developed MVC pages and frameworks for intranet applications including damage and loss, purchasing system and credit services.
-Product Maintenance:
Developed new functionalities and pages for the product maintenance application of Sunbelt Rentals ltd include, but not limited to, ranking order of products on customer facing website, add/edit product videos, managing technical specifications.}
Senior Web Developr (ASP MVC, C#, SQL, Entity Framework)
korr 2016 - korr 2017 (1 year)
-Developed where to buy section of TRW Aftermarket application in which the user current location is determined by the combination of HTML 5 geolocation method and a IP based location determination API, accordingly nearby shops are shown on the map based on the user search and filters.-. -Designed and developed the subscription and feedback sections of TRW Aftermarket. moreover, facilitated the process of delivering relevant technical articles to subscriptions based on multi-threading and HTML5.
Arsysco (WPF, MVVM, SQL Server)
jan 2013 - jan 2014 (1 year)
Identified a gap in the market. Produced a touch machine for ordering and paying foods in restaurants by credit cards without any intermediates which uses a dll to connect the application to the bank system, also I used WPF to design an optimized interface. It is operating in some famous restaurants.
Arsimi
PhD in Computer Science
(5 years)
MsC in Software Engineering
(2 years)
Kualifikimet
Research and Development Degree
Queen Mary University of London
2023
R&D Certificate
Botime
Opinion-Aware Retrieval Models Based on Sentiment and Intensity of Lexical Features
Modern Management based on Big Data II and Machine Learning and Intelligent Systems III
Sentiment analysis has received much attention in Information Retrieval (IR). However, when it comes to big data, incorporating sentiment of words into IR models becomes even more important, and as yet no widely accepted standard exists for this task. The contribution of this paper is a framework for quantifying term frequency (TF) variants with sentiments. We propose models derived from the strength of lexical features to improve sentiment-based ranking.
ADOR: A New Medical Dataset for Sentiment-based IR.
CIKM Workshops
Combining opinions such as user feelings with semantics would enhance the performance of these applications, especially. However, no widely medical benchmark is known for evaluating sentiment-aware IR. We create a dataset based on Amazon reviews for medical products and make it publicly available. We propose a sentiment-aware extension of TF. IDF. The benchmark could help healthcare organizations to effectively rank and filter urgent notifications.
Novel query performance predictors and their correlations for medical applications
Proc. 1st International Workshop on Generalization in Information Retrieval (GLARE 2018).
In this paper we propose novel pre-retrieval predictors that provides formal grounds for the development of a probabilistic framework which serves QPP with respect to various IR models. We explore the influences of different representations of information needs on forecasting the retrieval quality. Our study discusses the role of Average Term Frequency, Inverse Document Frequency and the dependency in the prediction. Furthermore, we empower the QPP tasks with a position-based TF-IDF measure.
Fdcm: Towards balanced and generalizable concept-based models for effective medical ranking
Proceedings of the 29th ACM International Conference on Information & Knowledge Management
We propose a new aggregation parameter to combine conceptual and term-based Dirichlet Compound Model scores effectively. The determination of this linear parameter is the result of exploring to what degree the difference of the conceptual and term-based sum of IDFs is influential to the integration. Instead of employing heuristics to find combined models, this paper aims to build the grounds for establishing reasonable aggregation standards based on semantic query performance predictors.
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