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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alqammaz, Abdullah Al-Mousa, Mohammad Rasmi Zaid, Ali Abu Asassfeh, Mahmoud Rajab, Mohammad Abdulrazzaq Dajah, Sherien Alali, Abdulla M. F. Khouj, Mohammed Zarqa University Cyber Security Department AzZarqa Jordan University of Anbar Computer Science Department Anbar Iraq The World Islamic Sciences & Education University Management Department Amman Jordan Zarqa University Computer Scince Department AzZarqa Jordan University of Business and Technology Electrical Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

The advent and increasing popularity of Android devices have brought about undesirable mobile malware and its attendant applications that constitutes a danger to society in terms of privacy and information leakage. Because the Android open-source OS is attractive to criminals, it seems impossible for old methods of virus detection to be effective as they used to be. The present study tackles this vital problem by offering an innovative enhancement that uses deep learning algorithms: recurrent neural networks and long short-term memory networks for android malware detection. New solid classification schema was created which focused on the meta-information of applications in order to differentiate entre benign and malicious apps. In our experiments, both RNN and LSTM models yielded improvements in performance metrics which suggests that they can be used to counter the current computer malware challenges. This study adds to the body of knowledge available on the detection of malware and lays the foundation for other studies designed to employ other couplings of deep learning algorithms in further boosting the security of Android applications. © 2024 IEEE.

关键词： Android malware

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Hybradization of Emperical Mode Decomposition and Machine Learning for Categorization of Cardiac Diseases 13

Hybradization of Emperical Mode Decomposition and Machine Le...

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13th IEEE International Conference on Electronics and Information Technologies, ELIT 2023

作者： Milyani, Danah Mohammad, Nouf Slama, Rim Qaisar, Saeed Mian Alhamdan, Alhanoof Hamour, Nora Computer Science Department Effat University Jeddah22332 Saudi Arabia Electrical and Computer Engineering Department Effat University Jeddah22332 Saudi Arabia CESI LINEACT Lyon69100 France

ISBN: (纸本)9798350383096

The arrhythmia is one of the cardiovascular diseases which has several types. In literature, researchers have presented a broad study on the strategies utilized for Electrocardiogram (ECG) signal investigation. Automated arrhythmia detection by analyzing the ECG data is reported using a number of intriguing techniques and discoveries. In order to effectively categorize arrhythmia, a novel approach based on the hybridization of the denoising filter, QRS complex segmentation, "Empirical Mode decomposition" (EMD), "Intrinsic Mode Functions" (IMFs) based features extraction, and machine learning techniques is developed in this study. To evaluate the categorization accuracy, the 10-fold cross validation (10-CV) strategy is used. Using an arrhythmia dataset that is publically available for research, the performance of our method is evaluated. A 97% average accuracy score is secured by our method for the problem of 5-class arrhythmias. These findings are comparable or better than counterparts. © 2023 IEEE.

关键词： Electrocardiograms

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Open Sesame! On the Security and Memorability of Verbal Passwords

Open Sesame! On the Security and Memorability of Verbal Pass...

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IEEE Symposium on Security and Privacy

作者： Eunsoo Kim Kiho Lee Doowon Kim Hyoungshick Kim Department of Electrical and Computer Engineering Sungkyunkwan University Electronics and Telecommunications Research Institute (ETRI) Department of Electrical Engineering and Computer Science University of Tennessee

ISBN: (数字)9798331522360

ISBN: (纸本)9798331522377

Despite extensive research on text passwords, the security and memorability of verbal passwords-spoken rather than typed-remain underexplored. Verbal passwords hold significant potential for scenarios where keyboard input is impractical (e.g., smart speakers, wearables, vehicles) or users have motor impairments that make typing difficult. Through two large-scale user studies, we assessed the viability of verbal passwords. In our first study (N = 2,085), freely chosen verbal passwords were found to have a limited guessing space, with 39.76% cracked within 10 9 guesses. However, in our second study (n = 600), applying word count and blocklist policies for verbal password creation significantly enhanced verbal password performance, achieving better memorability and security than traditional text passwords. Specifically, 65.6% of verbal password users (under the password creation policy using minimum word counts and a blocklist) successfully recalled their passwords in long-term tests, compared to 54.11% for text passwords. Additionally, verbal passwords with enforced policies exhibited a lower crack rate (6.5%) than text passwords (10.3%). These findings highlight verbal passwords as a practical and secure alternative for contexts where text passwords are infeasible, offering strong memorability with robust resistance to guessing attacks.

关键词： Resistance Data privacy Keyboards Passwords Speech recognition Motors Blocklists Numerical models Security Wearable devices

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Nuclei Pathology Classification using YoloV9

Nuclei Pathology Classification using YoloV9

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Singh, Deependra K. Anitha, G. Pattanaik, Balachandra Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Tamil Nadu Chennai601103 India School of Electrical and Computer Engineering Wollaga University P.O.BOX:395 Nekemte Ethiopia

ISBN: (纸本)9798350379716

Nuclei pathology is important for diagnosing various diseases and cancer. It is responsible for examining cellular structures found in tissue samples. Laboratories have the challenge of accurate nuclei classification in order to identify illnesses and support treatment. Here, we introduce a new strategy to identify four types of nuclei (,Other', 'Inflammatory', 'Epithelial' and 'Spindle-Shaped'), using YOLOv9 deep learning. The present approach provides an overall accuracy of 98.9% in classifying nuclei, with other metrics also ranked strongly. A high rate of consistency is guaranteed by the use of this model, meaning that the associated expertise is capable of increasing the diagnosis aid in pathology. Brought by the increase in precision, diagnosis in all other fields is also able to be increased more quickly. The utilization of YOLOv9 opens up a better approach to faster pipelines of better quality for the diagnosis of patients, where reliability and speed are the key to help guide proper funding to lived improvement outcomes. © 2024 IEEE.

关键词： Diseases

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Enhanced Layer Extraction for Efficient Plant Disease Classification using Efficientnet B1

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SN computer science 2025年第4期6卷 1-15页

作者： Thakur, Arastu Thakur, Abhilasha Vivek, V. Mahesh, T.R. Krishna, K. Murali Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-Be University) Bangalore 562112 India Department of Agriculture Marwadi University Gujarat Rajkot India Alliance School of Advanced Computing Alliance University Chandapura - Anekal Main Road Bengaluru Anekal 562106 India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-Be University) Bangalore 562112 India

Agriculture is crucial for the global economy, providing sustenance and resources for various industries. However, plant diseases threaten crop quality and yield, risking severe economic impacts. Traditional plant disease diagnosis methods, like manual inspection or basic machine learning, are slow, require expertise, and struggle with scalability and adapting to new diseases. This study introduces a deep learning model using convolutional neural networks (cnns) to autonomously detect and classify plant diseases from images, enhancing speed, scalability, and accuracy. We utilize the efficientnetb1 architecture, pretrained on imagenet and fine-tuned on a dataset of bell pepper, potato, and tomato diseases, recognizing 14 diseases plus healthy plants. The model achieves 98.80% accuracy on the test dataset, with high precision, recall, and F1-scores, demonstrating its effectiveness in plant disease diagnosis. This advancement marks a significant step in automating plant disease diagnosis, offering a robust tool for effective disease management in agriculture. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Agriculture Automated diagnosis Deep learning Disease management Efficientnetb1 Image classification Plant disease detection Transfer learning

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Towards Prevention of Sportsmen Burnout:Formal Analysis of Sub-Optimal Tournament Scheduling

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computers, Materials & Continua 2022年第1期70卷 1509-1526页

作者： Syed Rameez Naqvi Adnan Ahmad S.M.Riazul Islam Tallha Akram M.Abdullah-Al-Wadud Atif Alamri Department of Electrical and Computer Engineering COMSATS University IslamabadWah CampusWah47040Pakistan Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Research Chair of Pervasive and Mobile Computing King Saud UniversityRiyadh11543Saudi Arabia

Scheduling a sports tournament is a complex optimization problem,which requires a large number of hard constraints to *** the availability of several such constraints in the literature,there remains a gap sincemost of the new sports events pose their own unique set of requirements,and demand novel *** talking of the strictly time bound events,ensuring fairness between the different teams in terms of their rest days,traveling,and the number of successive games they play,becomes a difficult task to resolve,and demands *** this work,we present a similar situation with a recently played sports event,where a suboptimal schedule favored some of the sides more than the *** introduce various competitive parameters to draw a fairness comparison between the sides and propose a weighting criterion to point out the sides that enjoyed this schedule more than the ***,we use root mean squared error between an ideal schedule and the actual ones for each side to determine unfairness in the distribution of rest days across their entire *** latter is crucial,since successively playing a large number of games may lead to sportsmen burnout,which must be prevented.

关键词： Sports scheduling optimization constraint programming fatigue analysis

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Price Spike Classification and Regression Using A Hybrid Oversampling Method

Price Spike Classification and Regression Using A Hybrid Ove...

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2022 North American Power Symposium, NAPS 2022

作者： Zhang, Chenxu Gong, Lin Peng, Yehong Fu, Yong Mississippi State University Department of Electrical and Computer Engineering StarkvilleMS United States Jackson State University Department of Electrical & Computer Engineering and Computer Science JacksonMS United States

ISBN: (纸本)9781665499217

Price spikes are types of electricity prices that are much higher than normal electricity prices. However, due to low occurrence, price spikes are hard to be predicted by normal price forecast methods, which are designed to model patterns from the dataset with massive normal price cases. To facilitate price spike classification and regression, in this paper, a hybrid oversampling method is proposed to increase the number of price spike cases. As electricity prices are time series data, to enable that the synthetic price spike cases have the similar structure as the real ones, the enhanced structure preserving oversampling technique is applied to conserve the temporal relationships. Also, the synthetic minority oversampling method for regression is utilized to ensure the authenticity of the oversampled price spike values. Numerous case studies are used to evaluate the effectiveness of the proposed hybrid oversampling method, and the promising results demonstrate the capability of the proposed method to improve price spike classification and regression accuracy. © 2022 IEEE.

关键词： Support vector machines

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Application of reinforcement learning for energy consumption optimization of district heating system 32

Application of reinforcement learning for energy consumption...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Deng, Jifei Eklund, Miro Sierla, Seppo Savolainen, Jouni Niemistö, Hannu Karhela, Tommi Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Abo Akademi University Department of Information Technology Turku Finland Semantum Ltd Espoo Finland Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden

ISBN: (纸本)9798350399714

Heating residential spaces consumed 64 percent of total household energy consumption in Finland. Considering the heat transfer and time delay in the district heating system, the calculation of setpoints of supply temperature requires a comprehensive understanding of the real system, and experienced operators need to manually determine the setpoints. To save energy, a more effective and accurate method is needed for setpoints calculation. In this paper, a reinforcement learning based method is proposed. Through interacting with an Apros-based simulation model, the agents learn to calculate supply temperature parallelly for lowering energy costs. Simulation results show that the proposed method outperforms the existing method and has the potential to address the problem in real factories. © 2023 IEEE.

关键词： Reinforcement learning

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Advancing AI-Powered Medical Image Synthesis: Insights from MedVQA-GI Challenge Using CLIP, Fine-Tuned Stable Diffusion, and Dream-Booth + LoRA 25

Advancing AI-Powered Medical Image Synthesis: Insights from ...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Peter, Ojonugwa Oluwafemi Ejiga Rahman, Md Mahmudur Khalifa, Fahmi Department of Computer Science SCMNS School Morgan State University BaltimoreMD21251 United States Electrical & Computer Engineering Dept. School of Engineering Morgan State University BaltimoreMD21251 United States

The MEDVQA-GI challenge addresses the integration of AI-driven text-to-image generative models in medical diagnostics, aiming to enhance diagnostic capabilities through synthetic image generation. Existing methods primarily focus on static image analysis and lack the dynamic generation of medical imagery from textual descriptions. This study intends to partially close this gap by introducing a novel approach based on fine-tuned generative models to generate dynamic, scalable, and precise images from textual descriptions. Particularly, our system integrates fine-tuned Stable Diffusion and DreamBooth models, as well as Low-Rank Adaptation (LORA), to generate high-fidelity medical images. The problem is around two sub-tasks namely: image synthesis (IS) and optimal prompt production (OPG). The former creates medical images via verbal prompts, whereas the latter provides prompts that produce high-quality images in specified categories. The study emphasizes the limitations of traditional medical image generation methods, such as hand sketching, constrained datasets, static procedures, and generic models. Our evaluation measures showed that Stable Diffusion surpasses CLIP and DreamBooth + LORA in terms of producing high-quality, diversified images. Specifically, Stable Diffusion had the lowest Fréchet Inception Distance (FID) scores (0.099 for single center, 0.064 for multi-center, and 0.067 for combined), indicating higher image quality. Furthermore, it had the highest average Inception Score (2.327 across all datasets), indicating exceptional diversity and quality. This advances the field of AI-powered medical diagnosis. Future research will concentrate on model refining, dataset augmentation, and ethical considerations for efficiently implementing these advances into clinical practice. © 2024 Copyright for this paper by its authors.

关键词： Diagnosis

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Optimization of 3D Holographic Projection for a Medical Image System using Taguchi Method and Fuzzy Inference System

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Journal of Imaging science and Technology 2023年第4期67卷 1-7页

作者： Chou, Ming-Chung Huang, Tian-Hsiang Tsai, Jinn-Tsong Chen, Yen-Ming J. Hwang, Kao-Shing Huang, Wei-Tai Ho, Wen-Hsien Department of Medical Imaging and Radiological Sciences Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Computer Science and Artificial Intelligence National Pingtung University Pingtung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Department of Information Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Mechanical Engineering National Pingtung University of Science and Technology Pingtung Taiwan College of Professional Studies National Pingtung University of Science and Technology Pingtung Taiwan

Two dimensional projections of three dimensional (3D) objects are used to teach anatomy. These projections are relatively unrealistic, and medical students cannot easily and accurately grasp the position and shape of each anatomical structure. As a remedy, 3D holographic projections allow for 3D anatomical structures to be projected in space without the use of worn contraptions. This study designed a method for optimizing 3D holographic projections with respect to the quality characteristics of 3D imaging definition and stereoscopic quality. The Taguchi method was used to determine the factors that most greatly influenced these quality characteristics, and the Delphi method and fuzzy theory were used to quantify image quality. Among three main factors of material of the projection panel, thickness of the projection panel, and angle made by the projection panel with the horizontal plane, thickness was the most influential. The optimal combination of factors was acrylic material, a thickness of 1 mm, and an angle of 45°. This paper's method aids medical pedagogy and can be used by the holographic projection manufacturing industry to optimize 3D imaging definition and stereoscopic quality. © 2023 Society for Imaging science and Technology.

关键词： Taguchi methods

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