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2024 International Conference on computer and Applications, ICCA 2024

作者： Nassar, Seif T. Hamdy, Abeer Nagaty, Khaled Department of Computer Networks Faculty of Informatics and Computer Science Cairo Egypt

ISBN: (纸本)9798350367560

Decentralized applications (DApps) are increasingly using off-chain data, yet growing concerns about data privacy hinder their widespread adoption. Zero-knowledge proofs (ZKPs) have emerged as a solution to this problem. This paper proposes a novel hybrid framework that combines Zero-Knowledge Scalable Transparent Arguments of Knowledge (zk-STARKs) and Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs) to deliver an efficient, scalable, quantum-resistant, and privacy-preserving ZKP system. The framework employs zk-STARKs' ability to handle large computations with quantum resistance and zk-SNARKs' succinct proofs and fast verification. The hybrid framework utilizes advanced arithmetization techniques to balance scalability, privacy, and security, including Algebraic Intermediate Representation (AIR) for zk-STARKs and Rank-1 Constraint Systems (RlCS) or PLONKish constraints for zk-SNARKs. The framework uses the Kate-Zaverucha-Goldberg (KZG) polynomial commitment scheme for reliability and transparency and eliminates trusted setup by using Discrete-logarithm-based Argument of Recursive Knowledge (DARK) commitments and Poseidon hashing. This paper details the construction of the hybrid proof system, analyzes its complexity, and explores its potential applications. The framework solves smart contract data feed limitations, security, efficiency, and scalability while prioritizing privacy. © 2024 IEEE.

关键词： Differential privacy

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A Framework for Food Availability Forecasting Using Temporal Knowledge Graph

A Framework for Food Availability Forecasting Using Temporal...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Eltaki, Nada Nagaty, Khaled Elzanfaly, Doaa Maghawry, Noura The British University in Egypt Faculty of Informatics and Computer Science Department of Information System Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Science Department of Computer Science Cairo Egypt Helwan University Faculty of Informatics and Computer Science Department of Information System Cairo Egypt

ISBN: (纸本)9798350367560

The entire world encounters a significant problem which is food availability. 'Zero-hunger' is the second Sustainable Development Goal (SDG) of the goals established by the United Nations in 2015. With the increasing number of people and various factors affecting food availability such as climate and country-based food production rate, food forecasting models have become increasingly essential. However, traditional forecasting techniques are limited in dealing with the data and identifying the relationships between them within a specific timestamp. This paper proposes a framework for an intelligent food availability forecasting model built using deep learning techniques and based on a temporal knowledge graph where different timestamps are taken into consideration. The proposed framework helps in enhancing and overcoming the challenges and limitations of the recent forecasting techniques leading to a dynamic, scalable, intelligent forecasting model. © 2024 IEEE.

关键词： Prediction models

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Data-Driven Decision-Making for Bank Target Marketing Using Supervised Learning Classifiers on Imbalanced Big Data

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computers, Materials & Continua 2024年第10期81卷 1703-1728页

作者： Fahim Nasir Abdulghani Ali Ahmed Mehmet Sabir Kiraz Iryna Yevseyeva Mubarak Saif School of Computer Science and Informatics De Montfort UniversityLeicesterLE19BHUK Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia

Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance ***,imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics,limiting their overall *** study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers(SLCs)and evaluates their performance in data-driven *** evaluation uses various metrics,with a particular focus on the Harmonic Mean Score(F-1 score)on an imbalanced real-world bank target marketing *** findings indicate that grid-search random forest and random-search random forest excel in Precision and area under the curve,while Extreme Gradient Boosting(XGBoost)outperforms other traditional classifiers in terms of F-1 *** oversampling methods to address the imbalanced data shows significant performance improvement in XGBoost,delivering superior results across all metrics,particularly when using the SMOTE variant known as the BorderlineSMOTE2 *** study concludes several key factors for effectively addressing the challenges of supervised learning with imbalanced *** factors include the importance of selecting appropriate datasets for training and testing,choosing the right classifiers,employing effective techniques for processing and handling imbalanced datasets,and identifying suitable metrics for performance ***,factors also entail the utilisation of effective exploratory data analysis in conjunction with visualisation techniques to yield insights conducive to data-driven decision-making.

关键词： Big data machine learning data mining data visualization label encoding imbalanced dataset sampling techniques

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Combined deep convolutional neural networks for abnormality classification in wireless capsule endoscopy images

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Multimedia Tools and Applications 2025年 1-29页

作者： Garbaz, Anass Lafraxo, Samira Charfi, Said El Ansari, Mohamed Koutti, Lahcen Salihoun, Mouna LabSIV Department of Computer Science Faculty of Sciences Ibn Zohr University Agadir Morocco Informatics and Applications Laboratory Department of Computer Science Faculty of Sciences My Ismail University Meknes Morocco Faculty of Medicine and Pharmacy of Rabat Mohammed V University Rabat Morocco

A digestive disease is a condition that affects the gastrointestinal (GI) tract, ranging from mild to severe. Common issues include heartburn, cancer, irritable bowel syndrome, and lactose intolerance. Additionally, other intestinal concerns such as bleeding, polyps, celiac disease, Crohn’s disease, ulcers, diverticulitis, small bowel syndrome, and intestinal ischemia represent further digestive illnesses. Wireless capsule endoscopy (WCE) is a device designed for endoscopic exploration that employs a compact camera to capture images of the GI tract as it passes through. WCE significantly aids in the identification of GI tract disorders. However, due to the capsule’s rapid transit and the potential for blurry, ambiguous images, abnormalities in certain regions of the intestine may be overlooked. Moreover, frames can become hazy if obstructed by food or stool waste. In this research, we propose a method for detecting abnormalities in WCE images to address these limitations. To achieve high classification performance through the extraction of both high-level and low-level features, we employ a deep CNN that integrates the DenseNet121 model with a residual block. Experimental studies demonstrate that our proposed method outperforms state-of-the-art techniques on two publicly available WCE datasets, one focused on bleeding and the other on various abnormalities. The accuracy of the proposed architecture is 96.96% for the Kvasir-Capsule Dataset and 99.50% for the MICCAI 2017 dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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A comprehensive survey on weed and crop classification using machine learning and deep learning

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Artificial Intelligence in Agriculture 2024年第3期13卷 45-63页

作者： Faisal Dharma Adhinata Wahyono Raden Sumiharto Department of Computer Science and Electronics Faculty of Mathematic and Natural SciencesUniversitas Gadjah MadaYogyakartaIndonesia Faculty of Informatics Institut Teknologi Telkom PurwokertoCentral JavaIndonesia

Machine learning and deep learning are subsets of Artificial Intelligence that have revolutionized object detection and classification in images or *** technology plays a crucial role in facilitating the transition from conventional to precision agriculture,particularly in the context of weed *** agriculture,which previously relied on manual efforts,has now embraced the use of smart devices for more efficient weed ***,several challenges are associated with weed detection,including the visual similarity between weed and crop,occlusion and lighting effects,as well as the need for early-stage weed ***,this study aimed to provide a comprehensive review of the application of both traditional machine learning and deep learning,as well as the combination of the two methods,for weed detection across different crop *** results of this review show the advantages and disadvantages of using machine learning and deep ***,deep learning produced superior accuracy compared to machine learning under various *** learning required the selection of the right combination of features to achieve high accuracy in classifyingweed and crop,particularly under conditions consisting of lighting and early growth ***,a precise segmentation stage would be required in cases of *** learning had the advantage of achieving real-time processing by producing smaller models than deep learning,thereby eliminating the need for additional ***,the development of GPU technology is currently rapid,so researchers are more often using deep learning for more accurate weed identification.

关键词： Deep learning Machine learning Precision agriculture Weed control Weed detection

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Development of a machine learning model for predicting abnormalities of commercial airplanes

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Data science and Management 2024年第3期7卷 256-265页

作者： Rossi Passarella Siti Nurmaini Muhammad Naufal Rachmatullah Harumi Veny Fara Nissya Nur Hafidzoh Department of Computer Engineering Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Intelligent System Research Group(ISysRG) Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Department of Informatics Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia College of Engineering Studies Universiti Teknologi MARA(UiTM)Shah Alam40450Malaysia

Airplanes are a social necessity for movement of humans,goods,and *** are generally safe modes of transportation;however,incidents and accidents occasionally *** prevent aviation accidents,it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast *** study combined data-quality detection,anomaly detection,and abnormality-classification-model *** research methodology involved the following stages:problem statement,data selection and labeling,prediction-model development,deployment,and *** data labeling process was based on the rules framed by the international civil aviation organization for commercial,jet-engine flights and validated by expert commercial *** results showed that the best prediction model,the quadratic-discriminant-analysis,was 93%accurate,indicating a“good fit”.Moreover,the model’s area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96,respectively,thus confirming its“good fit”.

关键词： Automatic dependent surveillance-broadcast data Commercial airplanes accident Data-labeling Machine learning Prediction model

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Cross-Language Code Clone Detection Using Abstract Syntax Tree and Graph Neural Network 5

Cross-Language Code Clone Detection Using Abstract Syntax Tr...

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5th International Conference on computer and Applications, ICCA 2023

作者： Swilam, Zeina Hamdy, Abeer Pester, Andreas Faculty of Informatics & Computer Science Cairo Egypt

ISBN: (纸本)9798350303254

Code clones refer to code fragments that have similar functionality but may differ in syntax. When code duplication occurs, it can pose challenges during system maintenance and necessitate fixing errors in multiple locations. Existing methods for detecting code clones typically focus on clones within the same programming language. However, as the use of multiple programming languages becomes more prevalent, clones across different languages are becoming increasingly common. Recent research studies have explored the detection of cross-language code clones using Recurrent Neural Networks (RNN), specifically variants like LSTM and GRU. This paper presents an approach that combines the strengths of Abstract Syntax Trees (AST) and Graph Neural Networks (GNN) to identify cross-language code clones. The AST represents the code as a graph structure, while GNNs are capable of learning the state embeddings of each graph node, capturing information about its surroundings and the overall graph structure. Utilizing GNNs in the context of cross-language clone detection helps capture additional semantic information about the code fragments. Notably, GNNs have not been previously applied to the detection of cross-language code clones. Experimental results demonstrate that the proposed approach outperforms LSTM, GRU, and other state-of-the-art methods in terms of F1 score, precision, and recall. © 2023 IEEE.

关键词： Semantics

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Exploring the Intersection of Generative AI and Cognitive science: Insights and Implications 3

Exploring the Intersection of Generative AI and Cognitive Sc...

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3rd International Conference on Intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Sukthong, Napat Department of Computer Science Faculty of Informatics Mahasarakham University Mahasarakham Thailand

ISBN: (纸本)9798331529222

The rapid advancements in Generative Artificial Intelligence (AI) have revolutionized domains such as natural language processing, computer vision, and creative content generation. Simultaneously, Cognitive science seeks to understand the mechanisms of human cognition, including memory, decision-making, and creativity. This paper explores the intersection of these fields, investigating how Generative AI models can simulate cognitive processes and how Cognitive science insights can inform AI development. Methodologies include experiments with Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and GPT-3 to assess simulations of memory, creativity, and decision-making. Empirical findings demonstrate how VAEs enable memory reconstruction, GANs simulate decision-making processes, and Transformer-based models like GPT-3 exhibit creative capabilities. This study provides valuable insights into advancing AI research while deepening the theoretical understanding of human cognition. ©2024 IEEE.

关键词： Generative adversarial networks

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User Experience Design of Mobile Application for Tuberculosis Care and Prevention in Indonesia 2

User Experience Design of Mobile Application for Tuberculosi...

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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Felix Universitas Mikroskil Computer Science Program Informatics Faculty Medan Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351613

User experience (UX) is a major concern in m-health (mobile health) application design. In the global attempt to eliminate Covid-19, m-health apps play a remarkable role. With the same spirit, tuberculosis (TB) as the world's top infectious killer after Covid-19 can be suppressed. Unlike Covid-19 where the patient can be either cured or killed in less than a month, TB need at least 6 months to be cured for the first stage of treatment. TB are highly contagious and easily spread in the poor hygiene condition. The purpose of this research is to design a user-oriented UX design. The design then can ease programmers working with Health Ministry or healthcare units to develop a fully functioning m-health app. In order to produce a user-oriented UX design, the research uses design thinking methodology. © 2024 IEEE.

关键词： COVID-19

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PGV: An Edge Operator Based Random Point Localization Algorithm for Eye Tracking System 11

PGV: An Edge Operator Based Random Point Localization Algori...

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11th International Conference on Information Technology, computer and Electrical Engineering, ICITACEE 2024

作者： Al-Ikhsan, Hamdani Bachtiar, Fitra Abdurrachman Faculty of Computer Science Brawijaya University Informatics Department Malang Indonesia

ISBN: (纸本)9798350389289

Eye tracking is an important variable and offers benefits in Human-computer interaction (HCI) which is often complex and impractical in practice. Eye tracking implementation usually requires specialized devices, technology, or setup to work. On top of that, challenges like face obstructions like glasses or masks, differences in lighting, distance, and angles add another layer to the implementation difficulty. A novel random point detector algorithm called Pixel Gradient Value (PGV) is proposed which is an interest point detector-like algorithm to be used for eye-tracking purposes, which works by calculating the gradient value of a pixel in a given neighborhood by using an edge detection operator like Sobel edge detector and Canny edge detector as its basis. PGV is image-agnostic, it is capable of covering a wide amount of eye-tracking scenarios while running on a minimum setup. PGV is then used in a series of Fully Convolutional Network (FCN) machine learning system used for classification and point localization of the pupil center point. The proposed model is able to achieve higher results compared to the state-of-the-art result of em ax ≤ 0.025 value of 96.04, em ax ≤ 0.05 value of 98.71 and em ax ≤ 0.01 value of 99.19. © 2024 IEEE.

关键词： Convolution

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