@article{HanenbergMG24,

title = {Indentation and reading time: a randomized control trial on the differences 

 between generated indented and non-indented if-statements},

author = {Stefan Hanenberg and Johannes Morzeck and Volker Gruhn},

url = {https://doi.org/10.1007/s10664-024-10531-y},

doi = {10.1007/S10664-024-10531-Y},

year  = {2024},

date = {2024-01-01},

journal = {Empir. Softw. Eng.},

volume = {29},

number = {5},

pages = {134},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10401912,

title = {Revolutionizing Agriculture: Machine and Deep Learning Solutions for Enhanced Crop Quality and Weed Control},

author = {Syed Mujtaba Hassan Rizvi and Asma Naseer and Shafiq ur Rehman and Sheeraz Akram and Volker Gruhn},

doi = {10.1109/ACCESS.2024.3355017},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Access},

pages = {1-1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10510307,

title = {A Sequential VGG16+CNN based Automated Approach with Adaptive Input for Efficient Detection of Knee Osteoarthritis Stages},

author = {Shafiq Ur Rehman and Volker Gruhn},

doi = {10.1109/ACCESS.2024.3395062},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Access},

pages = {1-1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10509678,

title = {A Systematic Literature Review of the IoT in Agriculture—Global Adoption, Innovations, Security, and Privacy Challenges},

author = {Asma Naseer and Muhammad Shmoon and Tanzeela Shakeel and Shafiq Ur Rehman and Awais Ahmad and Volker Gruhn},

doi = {10.1109/ACCESS.2024.3394617},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Access},

volume = {12},

pages = {60986-61021},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Felsch:2023:CariesVisionTransformer,

title = {Detection and localization of caries and hypomineralization on dental photographs with a vision transformer model},

author = {Marco Felsch and Ole Meyer and Anne Schlickenrieder and Paula Engels and Jule Schönewolf and Felicitas Zöllner and Roswitha Heinrich-Weltzien and Marc Hesenius and Reinhard Hickel and Volker Gruhn and Jan Kühnisch},

url = {https://www.nature.com/articles/s41746-023-00944-2},

doi = {10.1038/s41746-023-00944-2},

isbn = {2398-6352},

year  = {2023},

date = {2023-10-25},

journal = {npj Digital Medicine},

volume = {6},

issue = {1},

pages = {198},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{diagnostics13233562,

title = {Automatized Detection of Periodontal Bone Loss on Periapical Radiographs by Vision Transformer Networks},

author = {Helena Dujic and Ole Meyer and Patrick Hoss and Uta Christine Wölfle and Annika Wülk and Theresa Meusburger and Leon Meier and Volker Gruhn and Marc Hesenius and Reinhard Hickel and Jan Kühnisch},

url = {https://www.mdpi.com/2075-4418/13/23/3562},

doi = {10.3390/diagnostics13233562},

issn = {2075-4418},

year  = {2023},

date = {2023-01-01},

journal = {Diagnostics},

volume = {13},

number = {23},

abstract = {Several artificial intelligence-based models have been presented for the detection of periodontal bone loss (PBL), mostly using convolutional neural networks, which are the state of the art in deep learning. Given the emerging breakthrough of transformer networks in computer vision, we aimed to evaluate various models for automatized PBL detection. An image data set of 21,819 anonymized periapical radiographs from the upper/lower and anterior/posterior regions was assessed by calibrated dentists according to PBL. Five vision transformer networks (ViT-base/ViT-large from Google, BEiT-base/BEiT-large from Microsoft, DeiT-base from Facebook/Meta) were utilized and evaluated. Accuracy (ACC), sensitivity (SE), specificity (SP), positive/negative predictive value (PPV/NPV) and area under the ROC curve (AUC) were statistically determined. The overall diagnostic ACC and AUC values ranged from 83.4 to 85.2% and 0.899 to 0.918 for all evaluated transformer networks, respectively. Differences in diagnostic performance were evident for lower (ACC 94.1-96.7%; AUC 0.944-0.970) and upper anterior (86.7-90.2%; 0.948-0.958) and lower (85.6-87.2%; 0.913-0.937) and upper posterior teeth (78.1-81.0%; 0.851-0.875). In this study, only minor differences among the tested networks were detected for PBL detection. To increase the diagnostic performance and to support the clinical use of such networks, further optimisations with larger and manually annotated image data sets are needed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hoss:2023:PeriodontalBoneLoss,

title = {Detection of Periodontal Bone Loss on Periapical Radiographs - A Diagnostic Study Using Different Convolutional Neural Networks},

author = {Patrick Hoss and Ole Meyer and Uta Christine Wölfle and Annika Wülk and Theresa Meusburger and Leon Meier and Reinhard Hickel and Volker Gruhn and Marc Hesenius and Jan Kühnisch and Helena Dujic},

doi = {10.3390/jcm12227189},

issn = {2077-0383},

year  = {2023},

date = {2023-01-01},

journal = {Journal of Clinical Medicine},

volume = {12},

number = {22},

abstract = {Interest in machine learning models and convolutional neural networks (CNNs) for diagnostic purposes is steadily increasing in dentistry. Here, CNNs can potentially help in the classification of periodontal bone loss (PBL). In this study, the diagnostic performance of five CNNs in detecting PBL on periapical radiographs was analyzed. A set of anonymized periapical radiographs (N = 21,819) was evaluated by a group of trained and calibrated dentists and classified into radiographs without PBL or with mild, moderate, or severe PBL. Five CNNs were trained over five epochs. Statistically, diagnostic performance was analyzed using accuracy (ACC), sensitivity (SE), specificity (SP), and area under the receiver operating curve (AUC). Here, overall ACC ranged from 82.0% to 84.8%, SE 88.8-90.7%, SP 66.2-71.2%, and AUC 0.884-0.913, indicating similar diagnostic performance of the five CNNs. Furthermore, performance differences were evident in the individual sextant groups. Here, the highest values were found for the mandibular anterior teeth (ACC 94.9-96.0%) and the lowest values for the maxillary posterior teeth (78.0-80.7%). It can be concluded that automatic assessment of PBL seems to be possible, but that diagnostic accuracy varies depending on the location in the dentition. Future research is needed to improve performance for all tooth groups.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{saeed_power_2023,

title = {The Power of Generative AI to Augment for Enhanced Skin Cancer Classification: A Deep Learning Approach},

author = {Mudassir Saeed and Asma Naseer and Hassan Masood and Shafiq Ur Rehman and Volker Gruhn},

url = {https://ieeexplore.ieee.org/document/10318035/},

doi = {10.1109/ACCESS.2023.3332628},

issn = {2169-3536},

year  = {2023},

date = {2023-01-01},

urldate = {2023-12-20},

journal = {IEEE Access},

volume = {11},

pages = {130330–130344},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/ese/SchurhoffHG23,

title = {An empirical study on a single company's cost estimations of 338 software 

 projects},

author = {Christian Schürhoff and Stefan Hanenberg and Volker Gruhn},

url = {https://doi.org/10.1007/s10664-022-10245-z},

doi = {10.1007/S10664-022-10245-Z},

year  = {2023},

date = {2023-01-01},

journal = {Empir. Softw. Eng.},

volume = {28},

number = {1},

pages = {11},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3532205,

title = {Software Engineering for Augmented Reality - A Research Agenda},

author = {Ingo Börsting and Markus Heikamp and Marc Hesenius and Wilhelm Koop and Volker Gruhn},

url = {https://doi.org/10.1145/3532205},

doi = {10.1145/3532205},

year  = {2022},

date = {2022-06-01},

journal = {Proc. ACM Hum.-Comput. Interact.},

volume = {6},

number = {EICS},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Augmented reality changes the way we perceive reality and how we interact with computers. However, we argue that to create augmented reality solutions, we need to rethink the way we develop software. In this paper, we review the state of the art in software engineering for augmented reality applications, derive open questions, and define a research agenda. For this purpose, we consider different engineering phases and evaluate conventional techniques regarding their applicability for AR development. In requirements engineering, we found the integration of AR experts and the associated collaboration between actors to be of key aspect in the development process. Additionally, requirements about the physical world must be considered, which in turn has a huge impact on UI design. The relevance of the physical environment is not yet sufficiently addressed in applicable techniques, which also applies to current implementation frameworks and tools, complicating the AR development process. When evaluating AR software iterations, we found interaction testing and test automation to have great potential, although they have not yet been sufficiently researched. Our paper contributes to AR research by revealing current core challenges within the AR development process and formulating explicit research questions that should be considered by future research.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Schoenwolf:2022:MIHDetection,

title = {Artificial intelligence-based diagnostics of molar-incisor-hypomineralization (MIH) on intraoral photographs},

author = {Jule Schönewolf and Ole Meyer and Paula Engels and Anne Schlickenrieder and Reinhard Hickel and Volker Gruhn and Marc Hesenius and Jan Kühnisch},

doi = {10.1007/s00784-022-04552-4},

year  = {2022},

date = {2022-05-24},

urldate = {2022-01-01},

journal = {Clinical Oral Investigations},

abstract = {The aim of this study was to develop and validate a deep learning--based convolutional neural network (CNN) for the automated detection and categorization of teeth affected by molar-incisor-hypomineralization (MIH) on intraoral photographs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}