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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Tarek, Hanan Khaled, Nada Aziz, Youanna Emadeldin, Engy Mekky, Radwa Mohammed, Ammar Waked, Imam MSA University Faculty of Computer Science Giza Egypt MSA University Faculty of Pharmacy Giza Egypt National Liver Institute Hepatology Department Cairo Egypt

ISBN: (纸本)9798350362633

Hepatocellular carcinoma (HCC) stands as the pre-vailing primary liver tumor, marked by elevated mortality rates. Around 80 % of instances emerge within cirrhotic livers, presenting a considerable obstacle in determining suitable therapeutic approaches. HCC can be classified into various categories, and by utilizing Barcelona Clinic Liver Cancer (BCLC) and Child- Pugh score stages, survival rate and duration for patients can be determined, as well as appropriate treatment plans devised. Several contributions are made by this paper. Firstly, the study partners with a hepatologist to pinpoint pertinent patient biomarkers used in forecasting Child-Pugh scores and BCLC stages. Subsequently, leveraging these predictions of stages and scores, personalized treatment strategies are suggested for patients. The dataset from the National Liver Institute in Egypt, comprising 1108 of patient records with 22 features, is evaluated using various machine learning models such as Random Forest, Gradient Boosting, Decision Trees, and Support Vector Machines (SVM). Results show that the Random Forest model achieves 92.77% accuracy in predicting Child-Pugh scores, while Gradient Boosting achieves 83.82 % accuracy in predicting BCLC stages. Subsequently, treatment plans are determined based on these stage and score predictions. © 2024 IEEE.

关键词： Support vector machines

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IoT-Integrated Convolutional Neural Network for Accurate Vehicle Acoustic Signal Analysis 3

IoT-Integrated Convolutional Neural Network for Accurate Veh...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Sasikala, P. Sharmini, K Subha Ponnappan, Venkatesan Sorakka Kavitha, K.R. Gunasekaran, K. Srinivasan, C. Department of Mathematics Salem India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Sathyabama Institute of Science and Technology Department of Mechanical Engineering Chennai India Sona College of Technology Department of Electronics and Communication Engineering Salem India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508456

This paper examines the use of IoT-integrated Convolutional Neural Networks (CNNs) for precise analysis of vehicle audio signals. The context highlights the growing intricacy of vehicle systems and the need for advanced monitoring methods. Acoustic signals generated by different vehicle components may provide significant insights about vehicle health;nevertheless, traditional analysis techniques are often ineffective. The Internet of Things (IoT) technology allows real-time data collection from sensors installed on vehicles, which is then analyzed using CNNs to identify certain patterns and abnormalities in auditory signals. The approaches used include using IoT sensors to record various vehicle noises and utilizing CNNs for feature extraction and classification, providing great defect detection precision. The results indicate the system's capacity to detect engine faults, braking deficiencies, and other mechanical concerns early, enabling quick treatments. It presents a novel methodology for vehicle monitoring, enhancing predictive maintenance, safety, and performance, with prospects for future developments in autonomous diagnostics. The proposed system improved vehicle acoustic signal processing using IoT and CNN models, achieving high accuracy of 99% in anomaly detection, preventive maintenance, and real-time traffic monitoring. © 2025 IEEE.

关键词： Convolutional neural networks

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Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

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computer Modeling in Engineering & sciences 2024年第6期139卷 3353-3385页

作者： Murugan Palanikumar Nasreen Kausar Dragan Pamucar Seifedine Kadry Chomyong Kim Yunyoung Nam Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Mathematics Faculty of Arts and ScienceYildiz Technical UniversityEsenlerIstanbul34220Turkey Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Yuan Ze UniversityTaoyuan City320315TaiwanChina Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos1102-2801Lebanon ICT Convergence Research Center Soonchunhyang UniversityAsan31538South Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538South Korea

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this *** is provided of the scoring function,accuracy function and operational laws of the log q-rung *** algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming ***,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify *** chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile *** numbers q are used to express the results of the *** demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

关键词： Vague set aggregating operators euclidean distance hamming distance decision making

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Intelligent Decision Support System for Crime Investigation

Intelligent Decision Support System for Crime Investigation

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2024 International Conference on computer and Applications, ICCA 2024

作者： Abdelshafy, Alaa Ezzat Elsawy, Ahmed Aboelyazed Amen, Shaimaa Talaat Benha University Faculty of Computers and Artificial Intelligence Department of Information Systems Benha Egypt Benha University Faculty of Computers and Artificial Intelligence Department of Computer Science Benha Egypt

ISBN: (纸本)9798350367560

Crime presents one of society's most formidable challenges, with its escalating rates significantly impacting individuals, families, and communities in detrimental ways. Therefore, efforts aimed at reducing the spread of crimes seek to reduce the spread of crimes by analyzing this large amount of data related to crime and accurately predicting it in real time. Thus, the competent authorities can take the necessary measures to prevent the occurrence of crime. Hence, advanced systems and contemporary methodologies for crime analysis are imperative to safeguard society. This paper presents an intelligent decision-support system in investigating crimes, using intensive crime data analysis to predict the quantity and types of crimes that occur at specific times and locations. Herein, Comparative analyzes are performed between machine learning and deep learning models, including Random Forest, K-NN, Naïve Bayes, ANN, Linear Regression, ARIMA, and LSTM, across datasets from the Urban Institute, Chicago, and Boston. The results indicate that linear regression outperformed the other methods in terms of regression, achieving higher R-squared values, particularly 0.946 and 0.476 for Chicago and the Urban Institute, respectively. On the other hand, LSTM exhibited in Boston by 0.525. For classification, Random Forest showed superior performance, with accuracies of 99.6%, 72.5%, and 78.6% for the Chicago, Boston, and Urban Institute datasets, respectively. © 2024 IEEE.

关键词： Deep learning

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On Computing Homological Hitting Sets 14

On Computing Homological Hitting Sets

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14th Innovations in Theoretical computer science Conference, ITCS 2023

作者： Bauer, Ulrich Rathod, Abhishek Zehavi, Meirav Department of Mathematics TUM School CIT Technische Universität München Germany Department of Computer Science Purdue University West LafayetteIN United States Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (纸本)9783959772631

Cut problems form one of the most fundamental classes of problems in algorithmic graph theory. In this paper, we initiate the algorithmic study of a high-dimensional cut problem. The problem we study, namely, Homological Hitting Set (HHS), is defined as follows: Given a nontrivial r-cycle z in a simplicial complex, find a set S of r-dimensional simplices of minimum cardinality so that S meets every cycle homologous to z. Our first result is that HHS admits a polynomial-time solution on triangulations of closed surfaces. Interestingly, the minimal solution is given in terms of the cocycles of the surface. Next, we provide an example of a 2-complex for which the (unique) minimal hitting set is not a cocycle. Furthermore, for general complexes, we show that HHS is W[1]-hard with respect to the solution size p. In contrast, on the positive side, we show that HHS admits an FPT algorithm with respect to p + ∆, where ∆ is the maximum degree of the Hasse graph of the complex K. © Ulrich Bauer, Abhishek Rathod, and Meirav Zehavi;licensed under Creative Commons License CC-BY 4.0.

关键词： Graph theory

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Automated Age and Gender Recognition in Networking Sites Using Variable Optimized CNN Model 5th

Automated Age and Gender Recognition in Networking Sites Usi...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Dutta, Supratik Chakraborty, Adrita Biswal, Ashish Kumar Tripathy, Hrudaya Kumar Obaid, Ahmed J. Alkhafaji, Mohammed Ayad Kalinga Institute of Industrial Technology Deemed to be University Odisha Bhubaneswar India Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq College of Technical Engineering National University of Science and Technology Dhi Qar Nasiriyah64001 Iraq

ISBN: (纸本)9789819771776

Due to the rising number of users especially in platforms like Facebook, YouTube, Instagram, and so on, automated classifying of age and gender detection has a lot of importance in this era. To implement this technology, the model is designed by detecting the age and gender of a person in separate entities to avoid confusion. Here deep learning algorithm convolutional neural network (CNN) has been used to address this problem. The OpenCV algorithm has also been used with a pre-trained Caffe model for age and gender detection. This model has the ability to identify numerous faces in a single image and forecast the gender and age of each face. The research paper showcases the rationale behind the selection of Caffe as the framework model over alternatives such as HAAR Cascade. Additionally, it presents empirical evidence indicating that the integration of OpenCV with Caffe Model yields superior outcomes compared to utilizing TensorFlow in conjunction with other models. From the 100 sample images that were taken for evaluation, an overall accuracy of 87% was achieved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution

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Computational Modeling of Streptococcus Suis Dynamics via Stochastic Delay Differential Equations

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computer Modeling in Engineering & sciences 2025年第4期143卷 449-476页

作者： Umar Shafique Ali Raza Dumitru Baleanu Khadija Nasir Muhammad Naveed Abu Bakar Siddique Emad Fadhal Department of Mathematics National College of Business Administration and EconomicsLahore54000Pakistan Center for Research in Mathematics and Applications(CIMA) Institute for Advanced Studies and Research(IIFA)University ofÉvoraRua Romão Ramalho59Évora7000-671Portugal Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102-2081Lebanon Department of Zoology University of SialkotSialkot51040Pakistan Department of Mathematics Air UniversityIslamabad44000Pakistan Department of Mathematics and Statistics College of ScienceKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Physical Sciences The University of ChenabGujrat50700Pakistan

Streptococcus suis(***)is a major disease impacting pig farming *** can also be transferred to humans by eating raw pork.A comprehensive study was recently carried out to determine the indices throughmultiple geographic regions in ***:The well-posed theorems were employed to conduct a thorough analysis of the model’s feasible features,including positivity,boundedness equilibria,reproduction number,and parameter *** Euler,Runge Kutta,and EulerMaruyama are some of the numerical techniques used to replicate the behavior of the streptococcus suis infection in the pig ***,the dynamic qualities of the suggested model cannot be restored using these ***:For the stochastic delay differential equations of the model,the non-standard finite difference approach in the sense of stochasticity is developed to avoid several problems such as negativity,unboundedness,inconsistency,and instability of the *** from traditional stochastic methods either converge conditionally or diverge over *** stochastic non-negative step size convergence nonstandard finite difference(NSFD)method unconditionally converges to the model’s true ***:This study improves our understanding of the dynamics of streptococcus suis infection using versions of stochastic with delay approaches and opens up new avenues for the study of cognitive processes and neuronal *** interaction behaviour and new solution comparison profiles.

关键词： Streptococcus suis disease model stochastic delay differential equations(SDDEs) existence and uniqueness Lyapunov function stability results reproduction number computational methods

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Geodesic Tracking of Retinal Vascular Trees with Optical and TV-Flow Enhancement in SE(2) 9th

Geodesic Tracking of Retinal Vascular Trees with Optical a...

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9th International Conference on Scale Space and Variational Methods in computer Vision, SSVM 2023

作者： van den Berg, Nicky J. Zhang, Shuhe Smets, Bart M. N. Berendschot, Tos T. J. M. Duits, Remco CASA Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Ophthalmology Department Maastricht University Maastricht Netherlands

ISBN: (纸本)9783031319747

Retinal images are often used to examine the vascular system in a non-invasive way. Studying the behavior of the vasculature on the retina allows for noninvasive diagnosis of several diseases as these vessels and their behavior are representative of the behavior of vessels throughout the human body. For early diagnosis and analysis of diseases, it is important to compare and analyze the complex vasculature in retinal images automatically. In previous work, PDE-based geometric tracking and PDE-based enhancements in the homogeneous space of positions and orientations have been studied and turned out to be useful when dealing with complex structures (crossing of blood vessels in particular). In this article, we propose a single new, more effective, Finsler function that integrates the strength of these two PDE-based approaches and additionally accounts for a number of optical effects (dehazing and illumination in particular). The results greatly improve both the previous left-invariant models and a recent data-driven model, when applied to real clinical and highly challenging images. Moreover, we show clear advantages of each module in our new single Finsler geometrical method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image enhancement

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FL@FM-TheWebConf’24: International Workshop on Federated Foundation Models for the Web 24

FL@FM-TheWebConf’24: International Workshop on Federated Fo...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： King, Irwin Long, Guodong Xu, Zenglin Yu, Han Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Faculty of Engineering and IT University of Technology Sydney Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798400701726

In the evolving landscape of machine learning research, two significant developments have emerged to prominence: foundation models and federated learning. The FL@FM-TheWebConf’24 workshop provides an exciting forum for researchers and practitioners to discuss promising works and chart potential trajectories forward for this emerging field. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Frequency modulation

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Food Item Recognition and Calories Estimation Using YOLOv5 5th

Food Item Recognition and Calories Estimation Using YOLOv5

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Shams, Mahmoud Y. Hussien, Ahmed Atiya, Aya Medhat, Logyn Bhatnagar, Roheet Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Department of Computer Science and Engineering Manipal University Jaipur Rajasthan India

ISBN: (纸本)9789819997039

This paper explores the application of the YOLOv5 algorithm for food recognition and calorie estimation. The study focuses on the specific context of Egyptian cuisine, utilizing a dataset dedicated to detecting Egyptian food. The paper outlines the data collection process, including techniques for labeling food items in images and data preprocessing steps. It provides a detailed explanation of the YOLOv5 architecture and its components, along with insights into the training process, transfer learning, and model tuning. The segmentation of the dataset into training and validation sets is discussed, along with the importance of selecting appropriate evaluation metrics for food detection. A step-by-step guide for training YOLOv5 on the Egyptian food dataset is presented, along with strategies for monitoring and improving the training process. Various metrics, such as accuracy, recall, and the interpretation of the confusion matrix, were used in the study for evaluating the performance of the trained model. The paper discusses the obtained results, identifies limitations and challenges encountered during the training and evaluation process, and suggests future improvements and areas for further research. Real-world applications of food detection using YOLOv5, including nutritional analysis and personalized nutrition tracking, are highlighted. The chapter concludes by summarizing the main points discussed and emphasizing the importance of food disclosure and the potential impact of YOLOv5 training on specific culinary traditions. Additionally, the paper introduces a separate chapter focused on calorie estimation. It covers the methodology for estimating calorie intake based on individual characteristics, such as age, gender, height, weight, and activity level. The integration of food recognition using YOLOv5 for adjusting calories is explained, emphasizing the creation of a comprehensive calorie database. This research contributes to the field of food recognition and cal

关键词： Nutrition

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