Osman Ali Sadek Ibrahim- PhD in Computer Science, School of Computer Science, The University of Nottingham
- Professor (Assistant) at Minia University
Osman Ali Sadek Ibrahim
- PhD in Computer Science, School of Computer Science, The University of Nottingham
- Professor (Assistant) at Minia University
Assistant Professor at Minia University, Egypt.
My website: https://orcid.org/0000-0001-9254-3093
About
55
Publications
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Introduction
My ORCID: 0000-0001-9254-3093
Research interests:
Intelligent Information Retrieval Systems and Data Mining. In December, 2017,
I had a PhD in Computer Science, ASAP Research group, School of Computer Science, The University of Nottingham, UK. In 2007, I had a M.Sc. in Computer Science Department, Minia University, Egypt, while in 2002, I obtained my B.Sc. In Maths & Computer Science, Faculty of Science, Minia University, Egypt.
Current institution
Additional affiliations
November 2002 - March 2018
October 2013 - December 2017
Publications
Publications (55)
Variable Neighborhood Search (VNS) optimizes heuristic solutions for daily problems by adjusting neighboring solutions' systematic changes. This study uses two adaptations of VNS, utilizing four random probability distributions and tradition random number generator to fine-tune offspring solutions. The first generates solutions using a singular pro...
Optimizing data features plays a crucial role in simplifying the process of selecting instances and analyzing datasets, especially when dealing with ranking problems. In scenarios such as ranking instances in medical diagnosis, search engine optimization , and information retrieval, there is a need for models that can rank data instances based on t...
Exploration and exploitation are fundamental concepts within the domain of Nature-Inspired Algorithms (NIAs) when optimizing solutions. Exploration aims to traverse a substantial portion of the solution space, whereas exploitation is directed at refining the current solution toward either local or global optima. For example, mutation represents an...
This paper provides a thorough review of recommendation methods from academic literature, offering a taxonomy that classifies recommender systems (RSs) into categories like collaborative filtering, content-based systems, and hybrid systems. It examines the effectiveness and challenges of these systems, such as filter bubbles, the "cold start" issue...
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterised by challenges in social communication, repetitive behaviours, and restricted interests [1]. Early and accurate diagnosis is critical for effective intervention, enabling individuals with ASD to achieve better developmental outcomes and an improved quality of life...
Information retrieval (IR) methodologies, empowered by computational intelligence (CI), hold a pivotal role across diverse research domains. CI encompasses a broad spectrum, including machine learning and evolutionary computing. When integrated with IR data representation paradigms, these methods are categorized by their application domains. Data r...
Misinformation can profoundly impact the reputation of an entity, and eliminating its spread has become a critical concern across various applications. Social media, often a primary source of information, can significantly influence individuals’ perspectives through content from less credible sources. The utilization of machine-learning (ML) algori...
![Evolution of emotion in various fields. Adapted from Ali et al. [4]](publication/378336281/figure/fig1/AS:11431281242375741@1715478867888/Evolution-of-emotion-in-various-fields-Adapted-from-Ali-et-al-4_Q320.jpg)
![Circumplex emotional model Acheampong et al. [2]](publication/378336281/figure/fig2/AS:11431281242444069@1715478868072/Circumplex-emotional-model-Acheampong-et-al-2_Q320.jpg)
![Lovheim’s emotional cube. Acheampong et al. [2]](publication/378336281/figure/fig3/AS:11431281242444070@1715478868249/Lovheims-emotional-cube-Acheampong-et-al-2_Q320.jpg)
![Plutchik’s emotion wheel of emotional models. Montero Quispe et al. [117]](publication/378336281/figure/fig4/AS:11431281242430118@1715478868464/Plutchiks-emotion-wheel-of-emotional-models-Montero-Quispe-et-al-117_Q320.jpg)
![Machine learning techniques explanation Liu and Lang [96]](publication/378336281/figure/fig5/AS:11431281242430119@1715478868690/Machine-learning-techniques-explanation-Liu-and-Lang-96_Q320.jpg)
Emotion is an interdisciplinary research field investigated by many research areas such as psychology, philosophy, computing, and others. Emotions influence how we make decisions, plan, reason, and deal with various aspects. Automated human emotion recognition (AHER) is a critical research topic in Computer Science. It can be applied in many applic...
Optimizing data instances play a crucial role in dealing with ranking problems. In scenarios such as ranking instances in medical diagnosis, search engine optimization, and information retrieval, there is a need for models that can rank data instances based on the significance of their features within the datasets. This paper provides a hybrid-box...
A Hybrid Box Multiobjective Evolutionary Gradient Strategy Approach for Learning to Rank Problem. The tool is novel in Learning to Rank and it contains novel gradient methodology in Information Retrieval and Continous optimization research fields in general
This concise paper introduces the inaugural Explainable and Interactive Learning to Rank (LTR) Package within the field of Information Retrieval (IR). The framework presented here is built upon the fusion of the Simulated Annealing Strategy with the (1+1)-Evolutionary Strategy, known as SAS-Rank, a ranking algorithm previously established in prior...
Grey wolf optimizer with adaptive upper and lower arrays bounds-based fitness performances
Grey wolf Strategy: Combining Grey Wolf with Evolutionary Strategy to adapt Lower and Upper bounds for better for Learning to Rank
A Normal Random Walk Grey Wolf optimizer that outperforms traditional Grey Wolf for Learning to Rank. Since, every nature population undergoes the Normal (Gaussian) Distribution, we replaced the the traditional random number generator by Normal Distribution as a Random Number Generator for Grey Wolf actions in the algorithm.
Probability Distributed Grey wolf optimizer Hybrid-Box tool for Ranking Preferences
Grey Wolf for Learning to Rank Problem is developed and customized by Osman Ali Sadek Ibrahim for Ranking Problem. Grey Wolf Algorithm is Proposed as a novel algorithm in Seyedali Mirjalili, Seyed Mohammad Mirjalili , Andrew Lewis, Grey Wolf Optimizer, Advances in Engineering Software 69 (2014). The code was developed and customized by me after che...
![Learning-to-rank process view according to [6].](publication/373644020/figure/fig1/AS:11431281415142620@1746028381108/Learning-to-rank-process-view-according-to-6_Q320.jpg){kind=icon}
In this research, the authors combine multiobjective evaluation metrics in the (1 + 1) evolutionary strategy with three novel methods of the Pareto optimal procedure to address the learning-to-rank (LTR) problem. From the results obtained, the Cauchy distribution as a random number generator for mutation step sizes outperformed the other distributi...
This is java archive package for Multi-Objective (1+1)-Evolutionary Strategy for Learning to Rank Problem. This is the first source code for multi-Objective Algorithm in Learning to Rank problem domain and it is linked to MDPI paper: Walaa N. Ismail, Osman A. S. Ibrahim, Hessah A. Alsalamah and Ebtesam Mohamed, Multi-Objective Learning to Rank Base...
The study proposes a novel model for DNA sequence classification that combines machine learning methods and a pattern-matching algorithm. This model aims to effectively categorize DNA sequences based on their features and enhance the accuracy and efficiency of DNA sequence classification. The performance of the proposed model is evaluated using var...
Metaheuristic applications for information retrieval research are limited in spite of the importance of this problem domain. Ranking the retrieved documents based on their importance is a vital issue for the scientific and industrial communities. This paper proposes a novel variable neighborhood search (VNS) algorithm with adaptation based on an ob...
Combining Variable Neighbourhood Search with Gradient Ascent for Learning to Rank Problem
DEEP Evolving Using Variable Neighbourhood Search for Learning to Rank Problem is created by Osman Ali Sadek Ibrahim.
The method used here for a random probability distribution for gene mutation step-size in every offspring.
DEEP Evolving Using Variable Neighbourhood Search for Learning to Rank Problem is created by Osman Ali Sadek Ibrahim. The method used here for each offspring has a different probability distribution for mutation than the other offsprings.
The first Explainable and Interactive Learning to Rank algorithm for better understanding of linear ranking models
Pattern matching is a highly useful procedure in several stages of the computational pipelines. Furthermore, some research trends in this research domain contributed to growing biological databases and updated them throughout time. This article proposes an comparison and analysis of different algorithms for match equivalent pattern matching like co...
Learning to rank (LTR) is the process of constructing a model for ranking documents or objects. It is useful for many applications such as Information retrieval (IR) and recommendation systems. This paper introduces a comparison between Offline and Online (LTR) for IR. It also proposes a novel Offline (1+1)-Simulated Annealing Strategy (SAS-Rank) a...
The is code for Learning to Rank Problem in Information Retrieval. It is a hybrid method of combining Simulated Annealing with (1+1)-Evolutionary Strategy. It uses 4 probability distribution for mutation Stepsize with based on 7 fitness evaluation metric by MIT as standard fitness and evaluation metrics.
This code is for (1+1)-Evolutionary Strategy for Learning to Rank and it is efficient package for better effectiveness and performance for large learning to rank datasets. It contains the capability for initialization from other linear ranking models and it uses 4 probability distribution for mutation step-sizes.
![Applications of pattern matching [32, 33]](publication/361893984/figure/fig1/AS:11431281111143599@1672888962242/Applications-of-pattern-matching-32-33_Q320.jpg)
At numerous phases of the computational process, pattern matching is essential. It enables users to search for specific DNA subsequences or DNA sequences in a database. In addition, some of these rapidly expanding biological databases are updated on a regular basis. Pattern searches can be improved by using high-speed pattern matching algorithms. R...
Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment,...
Nowadays, Autism Spectrum Disorder (ASD) is one of the primary psychiatric disorders illness that rapidly increases. One of the main problems of medical diagnosis data and classification is the variance in symptoms between patients. Thus, finding the discriminative symptoms that distinguish the illness accurately is an important issue. This paper w...
Most of query translation disambiguation approaches rely on a parallel corpus to resolve the translation ambiguity. This is because parallel corpus are good resources for resolving translation ambiguity. However, they have limitations in translating with a low convergence, specific domain, handling multi-word expressions, and non-availability in al...
In the context of Artificial Intelligence research, Evolutionary Algorithms and Machine Learning (EML) techniques play a fundamental role for optimising Information
Retrieval (IR). However, numerous research studies did not consider the limitation of using EML at the beginning of establishing the IR systems, while other research studies
compared EM...
Learning to Rank (LTR) is one of the problems attracting researchers in Information Retrieval (IR). The LTR problem refers to ranking the retrieved documents for users in search engines, question answering and product recommendation systems. There is a number of LTR approaches based on machine learning and computational intelligence techniques. Mos...
In the context of Artificial Intelligence research, Evolutionary Algorithms and Machine Learning (EML) techniques play a fundamental role for optimising Information Retrieval (IR). However, numerous research studies did not consider the limitation of using EML at the beginning of establishing the IR systems, while other research studies compared EM...
This abstract proposes a new technique for evolutionary library package that can be used in supervised classification problem. It is similar to deep learning approach for using deep learning strategy from random training samples from training dataset. The technique uses (1+1)-Evolutionary Strategy for taking some random samples to evolve the best c...
Learning to Rank (LTR) is one of the current problems in Information Retrieval (IR) that attracts the attention from researchers. The LTR problem is mainly about ranking the retrieved documents for users in search engines, question answering and product recommendation systems. There are a number of LTR approaches from the areas of machine learning...
http://www.cs.nott.ac.uk/~psxoi/freedocumentcollections.zip
ES-Rank: Evolutionary Strategy Learning to Rank tool. it is a jar file that can be used for learning to rank problems (including ranking in information retrieval, ranking in medical diagnosis among other problems). You can use the tool in windows and Linux command line. For usage details, you can start using this command in command line: java -jar...
In many contexts of Information Retrieval (IR), term weights play an important role in retrieving the relevant documents responding to users’ queries. The term weight measures the importance or the information content of a keyword existing in the documents in the IR system. The term weight can be divided into two parts, the Global Term Weight (GTW)...
In many contexts of Information Retrieval (IR), term weights play an important role in retrieving the relevant documents responding to users' queries. The term weight measures the importance or the information content of a keyword existing in the documents in the IR system. The term weight can be divided into two parts, the Global Term Weight (GTW)...
In the context of Information Retrieval (IR) from text documents, the term-weighting scheme (TWS) is a key component of the matching mechanism when using the vector space model (VSM). In this paper we propose a new TWS that is based on computing the average term occurrences of terms in documents and it also uses a discriminative approach based on t...
This paper introduces a new weighting scheme in information retrieval. It also proposes using the document centroid as a threshold for normalizing documents in a document collection. Document centroid normalization helps to achieve more effective information retrieval as it enables good discrimination between documents. In the context of a machine...
The Extensible Markup Language (XML) technology, with its self-describing and extensible tags, is significantly contributing to the next generation of semantic web. The present search techniques used for HTML and text documents are not efficient to retrieve relevant XML documents. Terms occurring in some places should have a greater influence than...
The task of an information retrieval system is to identify documents that will satisfy a user’s information needs. Effective fulfillment of this task has long been an active area of research, leading to sophisticated retrieval models for representing information content in documents and queries and measuring similarity between the two. The maturity...
In this paper we investigate a new fitness function for approximate information retrieval which is very fast and very flexible than cosine similarity fitness function. Also, we introduce the effects of choosing an initial population based on terms weights. The method is shown to be applicable to three well-known documents collections, where more re...
This study investigates the use of genetic algorithms in information retrieval. The method is shown to be applicable to three well-known documents collections, where more relevant documents are presented to users in the genetic modification. In this paper we present a new fitness function for approximate information retrieval which is very fast and...
This study investigates the use of genetic algorithms in information retrieval. The method is shown to be applicable to three well-known documents collections, where more relevant documents are presented to users in the genetic modification. In this paper we present a new fitness function for approximate information retrieval which is very fast and...
Indexing a document is the method for describing its content for sake of easier subsequent retrieval in a document storage. This paper describes the implementation of the automatic indexing of various term weighting schemes in an IR (Information Retrieval) system using CISI documents collection which constitutes of abstracts for information retriev...
Questions
Questions (192)
What is the difference between Manhattan distance and Hermann Minkowski distance as the less computational time fitness function?
Hello,
Please, What are the differences between black-box and white-box software packages?
Best regards,
Osman
Hello,
I developed Multi-objective Evolutionary Gradient Strategy for learning to rank, is there anyone can be co-author with me and can pay the MDPI fees?
This is the target special issue for the paper
and we will compare the Multi-objective Evolutionary Gradient Strategy with the paper published in the same issue. Since there is not any other Multiobjective Learning to Rank code package for research reproducible
Why Prophet Muhammad ordered Muslims to wear white clothes, while the weather in desert is hot and dried?
Hello everyone,
Is a woman need a full hijab from thermal isolated clothes in winter?
Hello,
I have a paper and I would like to submit it in :
I need co-author who can pay the APC for the journal.
Best regards,
Osman
Dear Colleagues,
My name is nominated to be a Topical Editor in Frontiers in Psychiatry journal which is Q2 Web of Science Psychiatry Journal and Frontiers in Neuroinformatic. The main topic of this special issue is Machine Learning methods for Autism Diagnosis problems.
I can give some ideas for that, since I am an Assistant Professor in the Computer Science Department, Faculty of Science, Minia University. The helping ideas as follows:
Comparing Ensemble methods for Autism Diagnosis, Since there is no research accomplished according to this research gap. In addition, every Automated Machine Learning package does not have the capability to compare all Ensemble methods together.
My research paper with my M.Sc. student compared traditional machine, transfer learning with only one package for Automated Machine Learning, while there are a number of Automated machine learning packages with various parameter tuning and various algorithms and there is no comparison for them on Autism Diagnosis.
The recent AI recent trend is Explainable and Interpretable machine learning for medical diagnosis problems
I can help you by research books for these ideas.
The theme of this special issue is
Improving Autism Spectrum Disorder Diagnosis Using Machine Learning Techniques
The goal of this Research Topic is to advance the field of ASD diagnosis by improving the accuracy, efficiency, and accessibility of machine learning techniques. We aim to address the challenges faced in utilizing sMRI and rsFC data for ASD diagnosis and explore novel approaches to enhance the diagnostic process. By integrating multidimensional data and refining machine learning algorithms, we strive for better diagnostic accuracy, early identification, and personalized treatment planning for individuals with ASD.
This Research Topic welcomes contributions that focus on, but are not limited to, the following themes:
Novel machine learning algorithms and techniques for ASD diagnosis
Integration of multimodal data (sMRI, rsFC, genetic information, etc.) for enhanced diagnostic accuracy
Development of interpretable machine learning models for clinical decision support
Identification and validation of robust biomarkers for ASD diagnosis
Exploration of large-scale datasets to improve machine learning models
Standardization and reproducibility in machine learning approaches for ASD diagnosis
We encourage authors to submit original research articles, reviews, opinion papers, and methodological studies that contribute to the advancement of ASD diagnosis using machine learning techniques.
Do we need Central cooling by CFC and Renewable Energy in an open-door and more secure cooling system?
Is a woman need a full hijab from thermally isolated clothes in summer?
