Yazar "Ali, Bassam S." seçeneğine göre listele

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    A novel approach for ensuring location privacy using sentiment analysis and analysis for health-care and Its effects on humans health
    (Amer Scientific Publishers, 2020) Ali, Bassam S.; Uçan, Osman Nuri; Bayat, Oğuz
    Twitter is the free report benefit for given important to as far as possible to 150 typescripts that may include script, pictures, videotapes and hyper-links. Persons sharing news, ideas and info to supports or agonists media. The most petrify theme is the persecution of ladies occurring a rounds the world. Individual's persecution of women of the web-based life to dependably impart utilizing coded words or to setting up their indirections nearness. This paper offering a novel procedure for feelings research on the maltreatment of women connected tweets and to organizing the suppositions with their geo-zones. The information mining calculations, for example, Bolster Vector Machine, Irregular Woodland, Sacking, Choice Trees and Most extreme Entropy are applying for extremity's based characterization of mistreatment of ladies related Tweets. The outcomes are looking at and exhibiting.
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    Development of a enhanced ear recognition system for personal identification
    (Ieee, 2019) Ali, Bassam S.; Uçan, Osman Nuri; Bayat, Oğuz
    This paper examines the development of a Match Region Localization (MRL) Ear Recognition System (ERS). Captured ear images were pre-processed through cropping, and enhancement. The pre-processed ear images were segmented by MRL segmentation algorithm, where the pre-processed images were sub divided into 96 sub images. Principal features from the segmented ear were extracted and used for template generation. K-nearest Neighbour classifiers with Euclidean distance metrics were used for the classification. The developed ERS demonstrated a recognition accuracy of 96.00%. The developed system can be tested on other available public ear databases to allow for cross-database comparison. Moreover, the system can still be improved for further reduction of ERS.
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    Lossy hyperspectral image compression based on intraband prediction and inter-band fractal
    (Association for Computing Machinery, 2018) Ali, Bassam S.; Uçan, Osman Nuri
    Fractal encoding promising proficiency in area of picture compressing but not used at compression of hyperspectral images. The paper presents a novel and applicable copy hyperspectral image lossy compressing founded in intra-prediction fractals bandwidth and hybrid between bands. The hyper spectral color picture is divided to different groups of bandings (GOB). So, the intraband estimate is used the first banding to each one GOB, overworking the spatial relation, as the form encrypting between banding through a resident exploration procedure is used to other bands at apiece (GOB), maximizing resident likeness among two together banding. The fractals constraints is contracted with coded Exponential-Golomb coding entropies. So, progress the decrypted value, the forecast mistake and the remaining fractal transform, quantize and encoded into entropy. Experimental compression results show that our scheme can achieve a actual high peak signal-to-noise ratio (PSNR) at low-slung bit degree and achieve a medium PSNR increase taking into account the overall bit complexity encoding rates compared to other lossless compression methods. Furthermore, the classification of the accuracy of our reconstructed image is 99.75%, which is better than the original uncompressed image. © Copyright 2018 ACM.