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  1. Article ; Online: A Study on Semantic Searching, Semantic Search Engines and Technologies Used for Semantic Search Engines

    Junaid Rashid / Muhammad Wasif Nisar

    International Journal of Information Technology and Computer Science , Vol 8, Iss 10, Pp 82-

    2016  Volume 89

    Abstract: Semantic search engines(SSE) are more efficient than other web engines because in this era of busy life everyone wants an exact answer to his question which only semantic engines can provide. The immense increase in the volume of data, traditional search ...

    Abstract Semantic search engines(SSE) are more efficient than other web engines because in this era of busy life everyone wants an exact answer to his question which only semantic engines can provide. The immense increase in the volume of data, traditional search engines has increased the number of answers to satisfy the user. This creates the problem to search for the desired answer. To solve this problem, the trend of developing semantic search engines is increasing day by day. Semantic search engines work to extract the best answer of user queries which exactly fits with it. Traditional search engines are keyword based which means that they do not know the meaning of the words which we type in our queries. Due to this reason, the semantic search engines super pass the conventional search engines because they give us meaningful and well-defined information. In this paper, we will discuss the background of Semantic searching, about semantic search engines; the technology used for the semantic search engines and some of the existing semantic search engines on various factors are compared.
    Keywords Semantic ; Semantic Searching ; Semantic Search Engine ; Hakia ; RDF. ; Electronic computers. Computer science ; QA75.5-76.95 ; Instruments and machines ; QA71-90 ; Mathematics ; QA1-939 ; Science ; Q
    Subject code 303
    Language English
    Publishing date 2016-10-01T00:00:00Z
    Publisher MECS Publisher
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

    Junaid Rashid / Saba Batool / Jungeun Kim / Muhammad Wasif Nisar / Amir Hussain / Sapna Juneja / Riti Kushwaha

    Frontiers in Public Health, Vol

    2022  Volume 10

    Abstract: Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific ... ...

    Abstract Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific disease prediction. In this paper, we propose a novel augmented artificial intelligence approach using an artificial neural network (ANN) with particle swarm optimization (PSO) to predict five prevalent chronic diseases including breast cancer, diabetes, heart attack, hepatitis, and kidney disease. Seven classification algorithms are compared to evaluate the proposed model's prediction performance. The ANN prediction model constructed with a PSO based feature extraction approach outperforms other state-of-the-art classification approaches when evaluated with accuracy. Our proposed approach gave the highest accuracy of 99.67%, with the PSO. However, the classification model's performance is found to depend on the attributes of data used for classification. Our results are compared with various chronic disease datasets and shown to outperform other benchmark approaches. In addition, our optimized ANN processing is shown to require less time compared to random forest (RF), deep learning and support vector machine (SVM) based methods. Our study could play a role for early diagnosis of chronic diseases in hospitals, including through development of online diagnosis systems.
    Keywords medical diagnosis ; feature selection ; chronic diseases ; artificial neural network (ANN) ; prediction ; Public aspects of medicine ; RA1-1270
    Subject code 006
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: A Study of Software Development Cost Estimation Techniques and Models

    Junaid Rashid / Muhammad Wasif Nisar / Toqeer Mahmood / Amjad Rehman / Syed Yasser Arafat

    Mehran University Research Journal of Engineering and Technology, Vol 39, Iss 2, Pp 413-

    2020  Volume 431

    Abstract: SDCE (Software Development Cost Estimation) has always been an interesting and budding field in Software Engineering. This study supports the SDCE by exploring its techniques and models and collecting them in one place. This contribution in the ... ...

    Abstract SDCE (Software Development Cost Estimation) has always been an interesting and budding field in Software Engineering. This study supports the SDCE by exploring its techniques and models and collecting them in one place. This contribution in the literature will assist future researchers to get maximum knowledge about SDCE techniques and models from one paper and to save their time. In this paper, we review numerous software development effort and cost estimation models and techniques, which are divided into different categories. These categories are parametric models, expertise-based techniques, learning-oriented techniques, dynamicsbased models, regression-based techniques, fuzzy logic-based methods, size-based estimation models, and composite techniques. Some other techniques which directly do not lie in any specific category are also briefly explained. We have concluded that no single technique is best for all situations; rather they are applicable in different nature of projects. All techniques have their own pros and cons and they are challenged by the rapidly changing software industry. Since no single technique gives a hundred percent accuracy, that is why one technique and model should not be preferred over all others. We recommend a hybrid approach for SDCE because in this way the limitations of one model and technique are complemented by the merits of the other model/technique. We also recommend a model calibration to obtain accurate results because if a model was developed in a different environment, we cannot expect reliable estimates from it in a completely new environment.
    Keywords Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Science ; Q
    Language English
    Publishing date 2020-04-01T00:00:00Z
    Publisher Mehran University of Engineering and Technology
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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