# Isparta University of Applied Sciences

**Type:** Institutions  
**Canonical URL:** https://scholariq.org/institutions/isparta-university-of-applied-sciences/

## Facts

| Field | Value |
| --- | --- |
| Avg h-index | 45 |
| Citations | 63,637 |
| City | Isparta |
| Country | TR |
| Description | Isparta University of Applied Sciences is a research organisation in Isparta, TR. OpenAlex records 7,448 works and 63,637 citations for it. 2,103 researchers list it as their most recent affiliation. |
| Homepage | https://www.isparta.edu.tr/ |
| OpenAlex ID | https://openalex.org/I4210118295 |
| Researchers | 2,103 |
| ROR ID | https://ror.org/02hmy9x20 |
| Type | education |
| Works | 7,448 |

## University papers

- [Citrus disease detection and classification using based on convolution deep neural network](https://scholariq.org/papers/citrus-disease-detection-and-classification-using-based-on-convolution-deep/)
- [ResUNet+: A New Convolutional and Attention Block-Based Approach for Brain Tumor Segmentation](https://scholariq.org/papers/resunet-a-new-convolutional-and-attention-block-based-approach-for-brain-tumor/)
- [DenseUNet+: A novel hybrid segmentation approach based on multi-modality images for brain tumor segmentation](https://scholariq.org/papers/denseunet-a-novel-hybrid-segmentation-approach-based-on-multi-modality-images/)
- [Classification of Knot Defect Types Using Wavelets and KNN](https://scholariq.org/papers/classification-of-knot-defect-types-using-wavelets-and-knn/)
- [Cataract disease classification from fundus images with transfer learning based deep learning model on two ocular disease datasets](https://scholariq.org/papers/cataract-disease-classification-from-fundus-images-with-transfer-learning-based/)
- [RECURRENT NEURAL NETWORK BASED MODEL DEVELOPMENT FOR WHEAT YIELD FORECASTING](https://scholariq.org/papers/recurrent-neural-network-based-model-development-for-wheat-yield-forecasting/)
- [Classification of Cataract Disease with a DenseNet201 Based Deep Learning Model](https://scholariq.org/papers/classification-of-cataract-disease-with-a-densenet201-based-deep-learning-model/)
- [CLASSIFICATION OF APPLE LEAF DISEASES USING THE PROPOSED CONVOLUTION NEURAL NETWORK APPROACH](https://scholariq.org/papers/classification-of-apple-leaf-diseases-using-the-proposed-convolution-neural/)
- [Classification of Poisonous and Edible Mushrooms with Optimized Classification Algorithms](https://scholariq.org/papers/classification-of-poisonous-and-edible-mushrooms-with-optimized-classification/)
- [MULTI-LABEL TEXT ANALYSIS WITH A CNN AND LSTM BASED HYBRID DEEP LEARNING MODEL](https://scholariq.org/papers/multi-label-text-analysis-with-a-cnn-and-lstm-based-hybrid-deep-learning-model/)
- [ABC+CNN-SH: Detection of peruvian coffea leaf diseases with a new hybrid classification algorithm based on ABC optimization and CNN](https://scholariq.org/papers/abc-cnn-sh-detection-of-peruvian-coffea-leaf-diseases-with-a-new-hybrid/)

## University researchers

- [Halıt Çetıner](https://scholariq.org/researchers/hal-t-cet-ner/)

## University top topics

Showing 8 of 22.

- [Thermodynamic and Exergetic Analyses of Power and Cooling Systems](https://scholariq.org/topics/thermodynamic-and-exergetic-analyses-of-power-and-cooling-systems/)
- [Plant Physiology and Cultivation Studies](https://scholariq.org/topics/plant-physiology-and-cultivation-studies/)
- [Forest ecology and management](https://scholariq.org/topics/forest-ecology-and-management/)
- [Essential Oils and Antimicrobial Activity](https://scholariq.org/topics/essential-oils-and-antimicrobial-activity/)
- [Fish Biology and Ecology Studies](https://scholariq.org/topics/fish-biology-and-ecology-studies/)
- [Insect-Plant Interactions and Control](https://scholariq.org/topics/insect-plant-interactions-and-control/)
- [Plant Pathogens and Fungal Diseases](https://scholariq.org/topics/plant-pathogens-and-fungal-diseases/)
- [Phytochemicals and Antioxidant Activities](https://scholariq.org/topics/phytochemicals-and-antioxidant-activities/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.