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Decision Aid Sciences and Application (DASA), International Conference on

作者： Senanur Armağan Esra Gündoğan Mehmet Kaya Dept. of Computer Engineering Firat University Elaziğ Turkey Dept. of Software Engineering Firat University Elaziğ Turkey

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Skin cancer, like all other types of cancer, is a health problem where early diagnosis saves the patient's life. Successful detection of skin lesions in dermoscopic images enables this process to be managed more quickly. Deep learning architectures have advanced significantly in recent years, particularly in the classification of medical images. For the multi-class classification of skin lesions, a hybrid model was developed in this study utilizing the convolutional neural network architectures of DenseNet and EfficientNet. In addition, SE (Squeeze and Excitation) attention mechanism is integrated into both models. Experimental results show that the inclusion of SE attention mechanism provides a noticeable improvement in classification accuracy. This study not only contributes to the development of effective skin lesion classification systems, but also It also provides valuable insights into the synergistic effects of combining neural network architectures with attention mechanisms.

关键词： Deep learning Training Visualization Attention mechanisms Accuracy Skin Data models Lesions Convolutional neural networks Skin cancer

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Frameworks, Applications and Challenges in Streaming Big Data Analytics: A Review 3

Frameworks, Applications and Challenges in Streaming Big Dat...

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3rd International Conference on Innovations in computer Science and software engineering, ICONICS 2022

作者： Nabeel Mustafa, Syed Muhammd Umer Farooque, Muhammad Tahir, Muhammad Khan, Shariq Mahmood Qamar, Rohail NED University of Engineering and Technology Dept. of Computer Science and I.T Karachi Pakistan Sir Syed University of Engineering and Karachi Dept. of Software Engineering Pakistan

ISBN: (纸本)9781665461412

Data output has increased dramatically in the twenty-first century. The speed at which data is introduced into the stream has increased as a result of the digitization of practically all industries. Big data refers to this enormous amount of data. A complex network of data with volume, velocity, diversity, authenticity, and value makes up this information. To derive useful insights from the data, it became necessary to examine the data. The term "streaming Big data"refers to data that changes very quickly. Big data analytics that are live or streaming have significantly improved analytics. Instantaneous analytics of the real-Time data are provided by streaming big data analytics, assisting the decision-makers. Big data analytics in real-Time has taken center stage in business. We thoroughly examine the phenomenon of streaming Big data analytics in this review study. We also look at the various streaming analytics frameworks in use. Additionally, we look into the fields in which streaming Big Data analytics are applied as well as the difficulties encountered. © 2022 IEEE.

关键词： Big data

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Defensive Reflexes of Drones

Defensive Reflexes of Drones

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International Conference on computer Theory and Applications (ICCTA)

作者： Adnan Akpınar Ensar Gul Dept. of Computer Engineering Maltepe University Istanbul Turkiye Dept. of Software Engineering Maltepe University Istanbul Turkiye

ISBN: (数字)9798331529680

ISBN: (纸本)9798331529697

Object detection is an image processing method that aims to detect the object from the images taken through cameras. In this context, it has become possible for drones to detect an object and predict the object’s distinctive features. In this study, object detection and classification methods were employed to enable drones to develop a defense mechanism by making a distinction between threatening and non-threatening objects approaching them, thereby autonomously protecting themselves, independently of the pilot, against potentially threatening objects.

关键词： Object detection Machine learning Feature extraction Cameras computer security Drones

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Academic Use of ChatGPT: Examining the Trends According to Disciplines

Academic Use of ChatGPT: Examining the Trends According to D...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Abdullah Bircan Dilek Mandal İrfan Kösesoy Dept. of Computer Engineering Kocaeli University Kocaeli Turkey Dept. of Software Engineering Kocaeli University Kocaeli Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

With the rapid advancement of large language models, particularly ChatGPT, have had a profound impact both in the academic world and in commercial applications. The innovative solutions offered by this technology in areas such as natural language processing, data analysis, and human-computer interaction have led to the emergence of new research domains across various disciplines. However, there has been no comprehensive examination in the literature of which academic disciplines focus on ChatGPT and which areas have attracted more *** study aims to systematically examine academic publications related to ChatGPT and to identify the disciplines in which this technology is more widely adopted, as well as the areas where intensive research is conducted. To this end, a specially developed tool was utilized to scan all academic publications pertaining to ChatGPT, allowing for a comprehensive interdisciplinary analysis. The results of the analysis reveal the academic fields in which ChatGPT is most intensively utilized, highlighting the differences among these fields. Notably, there is a concentration in categories such as “Medical Sciences,” “computer Sciences,” and “Education Sciences,” which also stand out among the most cited publications. Our findings indicate that ChatGPT has gained broad acceptance within the academic community, with certain disciplines leading the way in this regard. Furthermore, the interactions among categories sharing common keywords demonstrate how ChatGPT facilitates interdisciplinary knowledge flow.

关键词： Data analysis Large language models Chatbots Market research Data models

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Robust Psoriasis Severity Classification by using Data Augmentation

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Transactions of the Korean Institute of Electrical Engineers 2022年第12期71卷 1841-1847页

作者： Moon, Cho-I Baek, Yoo Sang Choi, Min Hyung Lee, Onseok Dept. of Software Convergence Graduate School Soonchunhyang University Korea Republic of Dept. of Dermatology Guro Hospital Korea University College of Medicine Korea Republic of Dept. of Computer Science and Engineering College of Engineering Design and Computing University of Colorado Denver United States

Psoriasis is a chronic recurrent disease formed by lesions such as erythema and scale. To evaluate the severity of psoriasis, the psoriasis area and severity index (PASI) score have been used in clinical trials and studies. This clinical indicator is subjective, so to overcome these shortcoming, various automatic psoriasis analysis methods based on deep learning have been studied. However, the limited number of data and psoriasis characteristic such as ambiguity of severity deteriorate model performance. One of the simple and powerful methods to overcome these problem is data augmentation. Data augmentation should be used according to data characteristics. Therefore, we analyzed and compared the classification results applied with five data augmentation methods, Geometric transformation, CutMix, Visual Corruptions, AutoAugment, RandAugment, and explored data augmentation method suitable for psoriasis severity classification. We used the EfficientNet B2 for psoriasis severity classification. As a result, when RandAugment or the combination of Geometric transform and Visual Corruptions were used, it showed the best classification performance with an accuracy of 87.5%. In addition, we confirmed the effect of data augmentation for improving model performance and the difference in performance according to single or multiple applications of the data augmentation methods. Through these results, our study can be applied to various studies as a data augmentation method suitable for psoriasis disease image. Copyright © The Korean Institute of Electrical Engineers.

关键词： Deep learning

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Comparing Emotion Detection Methods in Online Classrooms: YOLO Models, Multimodal LLM, and Human Baseline

Comparing Emotion Detection Methods in Online Classrooms: YO...

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IEEE Education engineering (EDUCON)

作者： Medha Mohan Ambali Parambil Salah Bouktif Munkhjargal Gochoo Fady Alnajjar Dept. Computer Science and Software Engineering United Arab Emirates University Al Ain UAE

ISBN: (数字)9798331539498

ISBN: (纸本)9798331539504

The COVID-19 pandemic has transformed learning environments, challenging educators to understand students' behaviors during the online mode of learning, in particular emotions associated with students' attention during virtual classrooms. As learning transitions between physical and virtual spaces, the ability to interpret student attention and engagement has become complex. In response to this challenge, our research investigates the use of GPT-4o, a multimodal large language model, for identifying student emotions by analyzing images in diverse learning settings. The study involved analyzing online classroom images featuring 149 faces, utilizing three distinct approaches: a computer vision model (YOLO), the multimodal LLM (GPT-4o), and a human-annotated baseline. The analysis systematically categorized facial expressions into eight emotional categories: Happy, Sad, Angry, Neutral, Contempt, Disgust, Fear, and Surprise. The findings indicate that multimodal LLMs can effectively detect student emotions, achieving an average accuracy of 93.8%, which aligns with the human baseline accuracy of 97.0%. In contrast, YOLO models maintained an average accuracy of 81.9%, performing well for basic emotions but struggling with subtle expressions. This research contributes to enhancing educational practices by providing valuable insights regarding the application of multimodal LLMs to assist educators in comprehending student emotions within both physical and digital classroom settings.

关键词： YOLO Emotion recognition Privacy Accuracy Annotations Pandemics Scalability Large language models Real-time systems Faces

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Investigating Online Searching Behavior Based on Google Trends in MENA Region Before and After COVID-19 53

Investigating Online Searching Behavior Based on Google Tren...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Belkacem, Abdelkader Nasreddine Jamil, Nuraini Alrabaee, Saed College of It Uae University Dept. of Computer and Network Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Information Systems and Security Al Ain United Arab Emirates

ISBN: (纸本)9798350336429

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning. © 2023 IEEE.

关键词： COVID-19 Google Trends MENA Online learning Remote learning Web Behavior

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Uplink Over-the-Air Aggregation for Multi-Model Wireless Federated Learning

arXiv

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arXiv 2024年

作者： Zhang, Chong Dong, Min Liang, Ben Afana, Ali Ahmed, Yahia Dept. of Electrical and Computer Engineering University of Toronto Canada Ericsson Canada Canada Dept. of Electrical Computer and Software Engineering Ontario Tech University Canada

We propose an uplink over-the-air aggregation (OAA) method for wireless federated learning (FL) that simultaneously trains multiple models. To maximize the multi-model training convergence rate, we derive an upper bound on the optimality gap of the global model update, and then, formulate an uplink joint transmit-receive beamforming optimization problem to minimize this upper bound. We solve this problem using the block coordinate descent approach, which admits low-complexity closed-form updates. Simulation results show that our proposed multi-model FL with fast OAA substantially outperforms sequentially training multiple models under the conventional single-model approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Masked Face Detection and Recognition Using a Unified Feature Extractor

Masked Face Detection and Recognition Using a Unified Featur...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Aroobah Iftikhar Arslan Shaukat Rimsha Tariq Dept of Computer and Software Engineering College of EME National University of Sciences and Technology (NUST) Islamabad Pakistan Dept of Computer Engineering Information Technology University Lahore Pakistan

The research field of computer vision has recently taken an interest in the active computer vision problem of masked face recognition due to the COVID-19 pandemic. The use of face masks as a preventative measure against the spread of COVID-19 has presented a new challenge for the technology of face recognition. Masked Face Recognition (MFR) is a specific type of facial occlusion issues that obstruct vital facial features such as the mouth, nose, or chin. The purpose of research on Masked Face Detection and Recognition is to fine-tune a pre-trained model that can more accurately recognize masked faces and detect whether the person is wearing a mask or not, which can be advantageous in various applications, like security and surveillance, healthcare, retail, law enforcement, the workplace, and social media. This paper proposes an approach to enhance the performance of the single neural network architecture as a unified model capable of both detection and recognition of masked face images by achieving 99% and 98% respectively, on the MFR2 dataset. Also, a detection model is proposed that is evaluated on publicly available datasets for the detection of masked faces, which are the MDMFR dataset, the Kaggle face mask detection dataset, and the Facedatahybrid dataset. The findings of this research offer valuable insights into the potential of a pre-trained network with transfer learning to make an efficient masked face detection and recognition system and pave the way for future research in this area.

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Exposing the Limits of Deepfake Detection using novel Facial mole attack: A Perceptual Black- Box Adversarial Attack Study

Exposing the Limits of Deepfake Detection using novel Facial...

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IEEE International Conference on Image Processing

作者： Qurat Ul Ain Ali Javed Khalid Mahmood Malik Aun Irtaza Dept of Computer Science University of Engineering and Technology Taxila Pakistan Dept of Software Engineering University of Engineering and Technology Taxila Pakistan Department of Computer Science and Engineering University of Michigan Flint MI USA

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Recently, we have observed an exponential growth in highly realistic deepfake videos, which are often used to spread disinformation, defame individuals, and even influence political outcomes. To combat these manipulated videos, researchers have proposed various deepfake detection techniques. Recent research has revealed that these detection techniques are vulnerable to different adversarial attacks. This paper examines the vulnerability of deepfake detectors to adversarial black-box attacks in terms of performing penetration testing to expose the existing defense benchmarks of current deepfake detectors. We present a perceptual facial mole black-box adversarial attack on deepfake detectors, where the attacker has limited knowledge of the architecture and settings of the detector. The proposed attack is visually natural and transferable based on the attention distraction mechanism, which distracts the model-shared attention patterns from the region of interest to other regions. We illustrate the efficacy of our attack on multiple cutting-edge deepfake detectors. This attack demonstrates that small perceptible perturbations that are visually natural on the facial face can disrupt and reduce the accuracy of the detectors significantly, up to 40.3%, with the highest success rate of 48.7%. Our findings highlight the necessity for proposing effective deepfake detectors that are resistant to black-box attacks.

关键词： Resistance Deepfakes Perturbation methods Image processing Closed box Detectors Media

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