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Learning and Technology Conference (L&T)

作者： Asaad B. Saad Eman Hossny Fatma A. Omara Software Engineering Department Faculty of Computers and Artificial Intelligence Cairo University Cairo Egypt Computer Science Department Faculty of Computers and Artificial Intelligence Cairo University Cairo Egypt

Distributed Denial of Service, known as DDoS attacks, significantly compromise network security and availability. Certain DDoS detection methods utilize machine learning techniques, whereas others are based on statistical techniques. Therefore, this study provides a classification of the currently available DDoS detection techniques based on these two methodologies, strengths, and limitations. In addition, a comparative study of some DDoS detection techniques proposed in recent years is presented. We obtained similar results in related studies using different datasets. Thus, the compared DDoS techniques were dataset-independent. Finally, this study highlights areas for further research and improvement.

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DM-Net for Colorectal Polyp Detection Using Dual Feature and Multi-Layer Aggregation

DM-Net for Colorectal Polyp Detection Using Dual Feature and...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Jingjing Wan Ling Wang Wenjie Zhu Bolun Chen Department of Gastroenterology The Second People's Hospital of Huai'an Huaian China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Early detection of colorectal polyps is crucial for preventing colorectal cancer. Although endoscopy is the current standard diagnostic method, it still faces challenges in terms of accuracy, efficiency, and patient comfort. To address these issues, this paper proposes a colorectal polyp detection model named DM-Net. This model utilizes the Dual Feature Integration Block (DFIB) to integrate channel and spatial features, enhancing feature extraction efficiency. Additionally, the model incorporates the Multi-Layer Path Aggregation Network (MLPAN) to handle the multi-scale variations of polyps. Experimental results demon-strate that DM-Net significantly outperforms existing methods in terms of accuracy and efficiency, offering high clinical applica-bility.

关键词： YOLO Accuracy Endoscopes Colorectal cancer Big Data Feature extraction Real-time systems Standards Faces

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Improving word embedding quality with innovative automated approaches to hyperparameters

Improving word embedding quality with innovative automated a...

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作者： Yildiz, Beytullah Tezgider, Murat Department of Software Engineering School of Engineering Atilim University Ankara Turkey Department of Computer Engineering Faculty of Engineering Firat University Elazig Turkey

Deep learning practices have a great impact in many areas. Big data and significant hardware developments are the main reasons behind deep learning success. Recent advances in deep learning have led to significant improvements in text analysis and classification. Progress in the quality of word representation is an important factor among these improvements. In this study, we aimed to develop word2vec word representation, also called embedding, by automatically optimizing hyperparameters. Minimum word count, vector size, window size, negative sample, and iteration number were used to improve word embedding. We introduce two approaches for setting hyperparameters that are faster than grid search and random search. Word embeddings were created using documents of approximately 300 million words. We measured the quality of word embedding using a deep learning classification model on documents of 10 different classes. It was observed that the optimization of the values of hyperparameters alone increased classification success by 9%. In addition, we demonstrate the benefits of our approaches by comparing the semantic and syntactic relations between word embedding using default and optimized hyperparameters. © 2021 John Wiley & Sons Ltd

关键词： Embeddings

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Ethics of AI: A Systematic Literature Review of Principles and Challenges 22

Ethics of AI: A Systematic Literature Review of Principles a...

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26th ACM International Conference on Evaluation and Assessment in software engineering, EASE 2022

作者： Khan, Arif Ali Badshah, Sher Liang, Peng Waseem, Muhammad Khan, Bilal Ahmad, Aakash Fahmideh, Mahdi Niazi, Mahmood Akbar, Muhammad Azeem M3S Empirical Software Engineering Research Unit University of Oulu Finland Faculty of Computer Science Dalhousie University Canada School of Computer Science Wuhan University China Department of Computer Science University of Loralai Pakistan College of Computer Science and Engineering University of Hail Saudi Arabia School of Business University of Southern Queensland Australia Department of Information and Computer Science King Fahd University of Petroleum and Minerals Finland Software Engineering Department Lappeenranta-Lahti University of Technology Finland

ISBN: (纸本)9781450396134

Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers, and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assesses the ethical capabilities of AI systems and provides best practices for further improvements. © 2022 ACM.

关键词： Challenges AI Ethics Machine Ethics Systematic Literature Review Principles

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Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits

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Multimedia Tools and Applications 2024年第33期83卷 80201-80223页

作者： Dutta, Ashit Kumar Raparthi, Mohan Alsaadi, Mahmood Bhatt, Mohammed Wasim Dodda, Sarath Babu Prashant, G.C. Sandhu, Mukta Patni, Jagdish Chandra Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Alphabet Life Science DallasTX7506 United States Department of Computer Science Al-Maarif University College Al Anbar31001 Iraq Model Institute of Engineering and Technology J&K Jammu India Software Engineer Central Michigan University DallasTX75009 United States Department of Computer Science Texas Tech University Lubbock79409 United States Skill Faculty of Engineering & amp Technology Shri Vishwakarma Skill University Haryana Palwal India CSE School of advance Computing Alliance University Bengaluru India

The neurological condition epilepsy is demanding and even fatal. Electroencephalogram (EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and different patients have various seizure activity patterns. EEG signal detection is time-consuming and labour-intensive, which puts a strain on medical staff and raises the possibility of erroneous detections. Usually, electrodes are inserted into the scalp or inside the brain for a brief period of time in order to obtain EEG data. It is essential to research efficient cross-patient automatic epilepsy detection techniques. The multi-head self-attention mechanism recognises long-distance dependencies with the same proficiency as it does temporal dynamic correlations between short-term temporal pattern characteristics and sequential relationships. The contextual representations are inputted into a bidirectional long short-term memory network (BiLSTM) so that information can be extracted in both directions. Classification and training are carried out utilising the log SoftMax algorithm. The experiments utilised scalp EEG data from the CHB-MIT database. Sensitivity, specificity, F1-score, and accuracy were computed to be 96.5 percent, 97.04 percent, 96.6 percent, and 96.2 percent, respectively. The results of the experiment show how well the technique works for detecting seizures in several patients utilising multi-channel EEG recordings. The results also demonstrate the method's improved generalisation capabilities and resilience and consistency in collecting seizure patterns. This is particularly critical for the tertiary diagnosis of epilepsy, and the findings indicate that the proposed method significantly improves the accuracy of detection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electroencephalography

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Emotion Based Signal Enhancement Through Multisensory Integration Using Machine Learning

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computers, Materials & Continua 2022年第6期71卷 5911-5931页

作者： Muhammad Adnan Khan Sagheer Abbas Ali Raza Faheem Khan T.Whangbo Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Progress in understanding multisensory integration in human have suggested researchers that the integration may result into the enhancement or depression of incoming *** is evident based on different psychological and behavioral experiments that stimuli coming from different perceptual modalities at the same time or from the same place,the signal having more strength under the influence of emotions effects the response *** research inmultisensory integration has not studied the effect of emotions despite its significance and natural influence in multisensory enhancement or ***,there is a need to integrate the emotional state of the agent with incoming stimuli for signal enhancement or *** this study,two different neural network-based learning algorithms have been employed to learn the impact of emotions on signal enhancement or *** was observed that the performance of a proposed system for multisensory integration increases when emotion features were present during enhancement or depression of multisensory signals.

关键词： Multisensory integration sensory enhancement depression emotions

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Adaptive Learning and Virtual Reality based Conceptual Framework for Dental Training

Adaptive Learning and Virtual Reality based Conceptual Frame...

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International Conference on computer and Applications (ICCA)

作者： Meriam Sherif Nahla Barakat Abeer Hamdy Department of Software Engineering Faculty of Informatics and Computer Sceince The British University in Egypt Cairo Egypt Department of Artifical Intellegence Faculty of Informatics and Computer Sceince The British University in Egypt Cairo Egypt Department of Software Enginerring Faculty of Informatics and Computer Sceince The British University in Egypt Cairo Egypt

Recently, there has been increasing interest in utilizing virtual reality (VR) technology in dental education and student training. VR based training can provide students with sufficient training and hands-on experience before entering the clinical setting, which in turn helps avoid risks ranging from small errors to severe consequences. However, previous research in this area is limited and none of the previous work proposed a complete, scalable framework for dental students’ education and training. This paper proposes an improved virtual environment for dental training, through an adaptive learning approach, integrated with the VR environment, which is - to the best of our knowledge - the first framework proposed for this purpose. The framework consists of eight modules that aim to provide students with a holistic dental training experience, equipping them with the essential skills and confidence required for their future clinical practice. Furthermore, it aims to improve patient care and safety. In order to guarantee the effectiveness and usability of the proposed framework, a thorough evaluation will be conducted, encompassing both a system perspective, which involves assessing software metrics, as well as evaluating its usability.

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An Efficient Routing Schema for Internet of Underwater Things/Ocean of Things

An Efficient Routing Schema for Internet of Underwater Thing...

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Wave Electronics and its Application in Information and Telecommunication Systems (WECONF)

作者： Reza Mohammadi Amin Nazari Behrooz Daneshmand Computer Engineering Faculty Bu-Ali Sina University Hamedan Iran Faculty of Software Engineering and Computer Engineering Saint Petersburg National Research University of Information Technologies Mechanics and Optics University ITMO Saint-Petersburg Russia

Recently, Internet of Underwater Things (IoUT) is a new technology used by researchers to explore underwater phenomena. Information is collected by underwater sensors and sent to the Ocean of Things (OoT) network, which is located on the water surface. OoT floaters then collect the data and send it to coastal stations via radio signals. Since the underwater environment is harsh for telecommunication, one of the current challenges in this architecture is the collected data transmission through the routes that can provide quality of service parameters (e.g., delay and packet loss). Hence, an optimal method is proposed in this article to reduce end-to-end delay and increase packet delivery rate in the hybrid networks of the internet of things and the ocean of things. In the proposed method, the network controller collects the underwater things data and creates the network graph using the capabilities of software-Defined Network (SDN) architectures. Then, the routing problem is modeled in the form of an Integer Linear Programming (ILP) model to improve the quality of service. The created model is then solved using the Golden Eagle Optimization (GEO) metaheuristic algorithm to find the optimal routes. Finally, the network controller installs the calculated routes on the underwater things to send information to the ocean of things floaters through these optimal routes. In the proposed method, the ocean of things floaters acts as fog nodes and send the received data to the coastal stations after preprocessing operations for analysis. Simulation results show that the proposed method can significantly improve the quality-of-service parameters.

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MQTT based monitoring and control system for remote stations in automotive industry

MQTT based monitoring and control system for remote stations...

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Denis Ivanović Josip Job Marko Puntarić Marijan Herceg CX TTTech-Auto Osijek Croatia Department of Software Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia Department of Communications Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

In this paper is presented implementation of a lightweight supervisory control and data acquisition system for remote test stations as a part of a larger test house in the automotive industry. It is necessary to allow remote set up and monitoring of the test stations, which means defining the structure of a system with numerous test stations as endpoints. The test stations are meant to be virtual machines but can be both virtual and physical machines. A communication protocol must be used that enables this within the organization’s network, regardless of actual distance and location. The parameters of the test stations to be monitored can be defined according to the needs and purpose. Even when creating your own tool, this function is not unique, i.e. on one test station parameters of operating system resources such as memory usage can be monitored, while on another test station logged-in users or running programs, reported system errors and others must be monitored. Data storage is one of the mandatory functions of this system. This process allows viewing data throughout history and more detailed analysis of events. There are numerous benefits that can be drawn and used from the data stored in the database. It is necessary to pay attention to the data stored as well as the scope and retention throughout history. The correct and secure setup of a database is one of the prerequisites for any serious system that processes data. Appropriate views should be created for users involved in the data monitoring process using all the elements mentioned so far. Creating a visually acceptable and easy-to-use interface that serves the main purpose is the final presentation of the entire system to users. A complete system leads to higher productivity, profitability and better organization of the company. It also allows project managers to better plan the use and occupancy of test stations. At any time, they have insight into the status of individual test stations, their activitie

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Local Weighted Neighborhood Rough Sets for Weakly Labeled Data

Local Weighted Neighborhood Rough Sets for Weakly Labeled Da...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Yanting Guo Meng Hu Qiang Liu Eric C.C. Tsang Xizhao Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Faculty of Innovation Engineering Macau University of Science and Technology Macau

Local rough set models are efficient rough data analysis methods for weakly labeled data in the rough set field. However, the existing local models do not distinguish the importance of different attributes to decisions when describing and defining binary relations, resulting in inaccurate information granules or classes, which affects the accuracy of concept approximation. In this paper, we choose local neighborhood rough sets as the representative model, and then propose Local Weighted Neighborhood Rough Sets (LWNRS). First, the importance of attributes to decisions is quantified by combining the supervised information of labeled data and the structural information of unlabeled data, and an attribute weighted method based on semi-supervised dynamic optimization is proposed. Meanwhile, the weighted neighborhood relation and neighborhood classes of objects are defined under weakly labeled data, and the WLNRS model is constructed. In addition, the weighted dependency is proposed to measure the data uncertainty, and the relationships among WLNRS and other relevant local and global models are deeply explored. Finally, the efficiency and accuracy of WLNRS in concept approximation are verified by comparison.

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