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2023 IEEE Renewable Energy and Sustainable E-Mobility Conference, RESEM 2023

作者： Rashid Al-Bakri, Ibrahim Mahmood Imran Ahmad, Muhammad Md Isa, Mohd Nazrin Zuhaer Nayef Al-Dabagh, Mustafa Universiti Malaysia Perlis Faculty of Electronic Engineering Technology Perlis Malaysia Telafer University Collage of Agriculture Telafer Iraq Knowledge University Department of Computer Science Erbil Iraq

ISBN: (纸本)9798350311327

With face recognition, machine learning is one of the computer sciences fields that is getting bigger the quickest. The goal of this study is to give a basic overview of machine learning and the algorithmic paradigms it provides. The study gives a detailed explanation of the basic ideas behind machine learning and the math that turns these ideas into methodologies that can be used in the real world, and discusses and compares the performance of various face recognition methods. Machine learning, a field of AI, has emerged as an important part of the digitizing approaches that have attracted a lot of interest. The purpose of this work is to provide a high-level overview of several of the most widely utilized and commonly used algorithmic techniques for machine learning currently available. The goal of this work is to help readers make educated decisions about the best algorithm for machine learning they should employ for a given task by highlighting the benefits and drawbacks of each method from an implementation point of view. © 2023 IEEE.

关键词： Decision trees

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Fast and Accurate Community Detection in Social Media Networks with a Multi-Algorithmic Approach

Fast and Accurate Community Detection in Social Media Networ...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： P Pabitha G Eswari Petchiammal Department of Computer Technology Anna University Chromepet India Department of Computer Science and Engineering Knowledge Institute of Technology Salem India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Community detection is an essential technique for uncovering structural patterns in complex networks, such as social, biological, and information networks. In this paper to explore a hybrid approach to community detection by combining the label propagation algorithm and leiden algorithm. Although LPA is well known for its computational effectiveness in large- scale networks, because of its reliance on local label propagation, it frequently results in unstable and fractured communities. High- quality community structures are produced by the Leiden algorithm, which is more computationally expensive than the Louvain method but ensures well-connected communities by optimising modularity. Our goal in combining these two approaches is to take use of LPA's speed for quick community discovery and the Leiden algorithm's refinement powers for increased accuracy and stability. Large or dynamic networks where both real-time speed and community quality are crucial are best suited for the suggested hybrid method. The combination of these algorithms yields more cohesive and modular communities than either one working alone, as evidenced by experimental results on synthetic social network data. The method is a useful tool for large- scale network analysis because it strikes a balance between the necessity for strong and significant community structures and computational efficiency.

关键词： Accuracy Social networking (online) Heuristic algorithms Scalability Stability analysis Biology Real-time systems Partitioning algorithms Computational efficiency Recommender systems

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Hybrid Classification Model Development for Enhancement the Data Driven Based Human Falling Detection Performance 3rd

Hybrid Classification Model Development for Enhancement the ...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Ahmed, Saadaldeen Rashid Fadel, Hala Adnan. Ismael, Omar Ayad Sulaiman, Sazan Kamal Mahdi, Nidhal Raoof Ahmed, NoorUlhuda S. Abubaker, Brwa Abdulrahman Hussain, Abadal-Salam T. Artificial Intelligence Engineering Department College of Engineering Alayan University Nasiriyah Iraq Department of Laser Optoelectronic Engineering/Al-Ma’moon University College/Al-Washash Baghdad Iraq Department of Computer Science College of Sciences University of Diyala Diyala Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Medical Laboratories Techniques Al Hikma University College Baghdad Iraq College of Medicine University of Al-Ameed PO Box 198 Karbala Iraq Computer Science Department Bayan University Kurdistan Erbil Iraq Department of Medical Instrumentation Techniques Engineering Technical Engineering College Al-Kitab University Altun Kupri Kirkuk Iraq

ISBN: (纸本)9789819624676

It is thus important to identify the cause of a fall in order to be able to protect the lives of people, especially the elderly and hospitalized patients who are most likely to fall. Three fall detection models are compared in this paper: These are the auto-encoder (AEC), the feed forward neural network (FFNN), and the particle swarm optimization-feed forward neural network (PSO-FFNN). The idea is to assess how well these models identify falls, perform, and are suitable for deployment in real-life scenarios. This study was based on data from 17 candidates, and the four classes of falls that were considered here include: Based on this dataset, the models were trained and evaluated with the emphasis made on the correct classification of falls and their differentiation from other movements. From the results presented, all the three models show encouraging signs in fall detection. The PSO-FFNN model, with 98% of accurate optimal rate, however, gave the highest accuracy level. By this result, it is clear that the PSO-FFNN model is highly reliable and accurate in detecting falls. The objective of this research was to develop more precise and credible forecasts of various types of motion turbulence employing the autoencoder’s unsupervised learning and FFFNN’s partitioned classification approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unsupervised learning

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Towards A knowledge Graph-based Exploratory Search for Privacy engineering 8

Towards A Knowledge Graph-based Exploratory Search for Priva...

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8th International Workshop on the Visualization and Interaction for Ontologies, Linked Data and knowledge Graphs, VOILA! 2023

作者： Herwanto, Guntur Budi Ekaputra, Fajar J. Piroi, Florina Sabou, Marta Faculty of Computer Science University of Vienna Austria Department of Computer Science and Electronics Universitas Gadjah Mada Indonesia Institute of Information Systems Engineering Faculty of Informatics TU Wien Austria Institute of Data Process and Knowledge Management WU Wien Austria

The concept of privacy-by-design has gained considerable attention in the wake of legal requirements that software systems should fulfill. The interconnected knowledge on privacy concepts, legal requirements, and software development artifacts required to implement this concept can be a major burden for organizations. To address this challenge, we propose a knowledge graph-based Exploratory Search system to help organizations complete privacy engineering tasks. We identify major requirements of such systems and develop an Exploratory Search prototype built on privacy engineering knowledge that integrates different information sources. While still preliminary, this work serves as a foundation for future research on integrating privacy knowledge into software development and demonstrates the potential of Exploratory Search Systems to support the cognitive process in privacy tasks such as privacy threat identification. © 2023 Copyright for this paper by its authors.

关键词： knowledge graph

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Osteoporotic Vertebral Fracture Classification in X-rays Based on a Multi-modal Semantic Consistency Network

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Journal of Bionic engineering 2022年第6期19卷 1816-1829页

作者： Yuzhao Wang Tian Bai Tong Li Lan Huang College of Computer Science and Technology Jilin UniversityChangchun130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China Department of Orthopedics The Second Hospital of Jilin UniversityChangchun130012China

Osteoporotic Vertebral Fracture(OVFs)is a common lumbar spine disorder that severely affects the health of *** a clear bone blocks boundary,CT images have gained obvious advantages in OVFs *** with CT images,X-rays are faster and more inexpensive but often leads to misdiagnosis and miss-diagnosis because of the overlapping *** how to transfer CT imaging advantages to achieve OVFs classification in X-rays is *** this purpose,we propose a multi-modal semantic consistency network which could do well X-ray OVFs classification by transferring CT semantic consistency *** from existing methods,we introduce a feature-level mix-up module to get the domain soft labels which helps the network reduce the domain offsets between CT and *** the meanwhile,the network uses a self-rotation pretext task on both CT and X-ray domains to enhance learning the high-level semantic invariant *** employ five evaluation metrics to compare the proposed method with the state-of-the-art *** final results show that our method improves the best value of AUC from 86.32 to 92.16%.The results indicate that multi-modal semantic consistency method could use CT imaging features to improve osteoporotic vertebral fracture classification in X-rays effectively.

关键词： Osteoporotic vertebral fracture classification Cross-modality Unsupervised domain adaptation Transfer learning Convolutional neural network computer-aided diagnosis

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Innovative Approaches to the Use of Multifunctional Materials in Building Envelopes 7th

Innovative Approaches to the Use of Multifunctional Material...

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7th International Conference on Construction, Real Estate, Infrastructure, and Project Management, ICCRIP 2023

作者： Kumari, Soni Suresh Kumar, R. Abhishek, Kumar Pathak, B.N. Nijhawan, Ginni Khayoon, Ali Talib Rajalakshmi, B. Kalra, Ravi Department of Mechanical Engineering GLA University UP Mathura India Department of Civil Engineering Institute of Aeronautical Engineering Hyderabad500043 India Gujarat Ahmedabad380026 India Department of Mechanical Engineering IMS Engineering College Ghaziabad India Lovely Professional University Panjab Phagwara India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq Department of Computer Science New Horizon College of Engineering Bangalore India Lloyd Institute of Engineering and Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India

ISBN: (纸本)9789819649013

Multifunctional materials have become a hot topic in building envelope design due to their ability to simultaneously perform several functions such as structural support, insulation, energy harvesting, and storage. This paper reviews recent developments in the use of multifunctional materials in building envelopes, including their composition, properties, and potential applications. The paper also examines innovative approaches to integrating these materials into building envelopes to enhance their performance and sustainability. The potential benefits and challenges of using multifunctional materials in building envelopes are discussed, along with future directions for research in this field. However, there are also challenges associated with the use of these materials, including their high cost, complexity of integration, and potential durability issues, which require further research and development. Overall, this review provides insights into the current state and potential of multifunctional materials in building envelopes, highlighting opportunities for future research and application. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Thermal comfort

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Exploring Deep Insights from Vast Data: An Overview of Deep Learning Techniques for Big Data

Exploring Deep Insights from Vast Data: An Overview of Deep ...

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International Conference on Electrical engineering and computer science (ICECOS)

作者： Sarhad Baez Hasan Shahab Wahhab Kareem Didar Dlshad Hamad Ameen Computer Science Department Soran University Faculty of Science Soran Iraq Department of Information System Engineering Department of Computer Engineering Erbil Polytechnic University College of Engineering Knowledge University Erbil Iraq

ISBN: (数字)9798350368253

ISBN: (纸本)9798350368260

Big data has emerged very fast, and this has brought both opportunities and problems that are related to the application of deep learning. This paper explores how deep learning can be implemented using big data and in particular the Apache Mahout library. Unlike most other machine learning tools, Mahout can perform distributed computation, especially while using the MapReduce frameworks including Apache Hadoop and Apache Spark. However, this study does not only focus on the explanation of the need for Mahout's integration and employment but also carries out a high-fidelity argument on how deep learning algorithms can be incorporated in the Mahout framework to optimize the manipulation of large and complicated datasets. Mahout-based contributions include the comparative analysis of key deep learning paradigms including distributed processing, parallel processing and transfer learning. This paper also includes examples of the Mahout deep learning integration use and its application in real contexts. This article describes the challenges that currently organizations face in handling big data and indeed, the potential threats these challenges pose to the success of deep learning techniques when used in organizations’ big data processing and analysis.

关键词： Deep learning Scalability Transfer learning Cluster computing Organizations Big Data Parallel processing Sparks Security Research and development

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A Novel Hybridclustering Model Forwireless Sensor Networks

A Novel Hybridclustering Model Forwireless Sensor Networks

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2023 International Conference on Artificial Intelligence and knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Vijayalakshmi, P. Saravanan, M. Rani, M. Thillai Ashok, M. Palaniappan, Ramya Nagaraj, V. Department of Computer Science and Engineering Knowledge Institute of Technology Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Ece Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Cse Madanapalle Institute of Technology and Science Andhrapradesh Chitoor India Department of Ece Annapoorna Engineering College Tamil Nadu Salem India

ISBN: (纸本)9798350334357

Energy efficiency is the largest critical provocation in the wireless sensor network. Communicationenergy is the ultimate consuming work in sensor junctions, especially in the great distance. Thus, clustered routing modelsaresystematicapproachesutilizedfor lowering the communication energy and expanding the lifespan of the networkwhichis the objective of this paper. This work proposes a hybrid clustered routingmodel for energyexpansion in WSN. Several clustering designs examine the residual power and distance in the middle of the junctions in the cluster heads selection and further rotate the cluster heads selection periodically. The proposed clustering design, K-Means, and LEACH protocol techniques are applied. The clustering planpermitsforreducing intra-cluster transmissions and to gain powerability for sensor junctions. The hybrid integration of K-means and Leach protocol is used toimprove the nodes' power and the network lifespan. © 2023 IEEE.

关键词： Wireless sensor networks

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Toward High-Accuracy 3D Creation of Aviation Tools via Analysis-by-Synthesis

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IEEE Access 2025年 13卷 87208-87219页

作者： Gundu Mohamed Hassan Yoo Sung Kim Geun Sik Jo Department of Artificial Intelligence Engineering Knowledge Management System Laboratory Inha University Incheon South Korea Department of Computer Science Engineering Artificial Intelligence Laboratory Inha University Incheon South Korea Augmented Knowledge Corporation Incheon South Korea

The field of education is rapidly shifting toward digital platforms; however, aviation education continues to rely heavily on traditional paper-based manuals. This persistence is largely due to the inherent complexity, scale, and high costs associated with aviation equipment, making digital transformation particularly challenging. In this paper, we utilizing the analysis-by-synthesis method is presented for multi-view 3D reconstruction of solid objects, specifically focusing on aviation tools. To facilitate this research, a custom dataset was generated using Unity, enabling controlled experimentation and performance evaluation. Comparative analysis with existing benchmarks demonstrates that, despite the intricate nature of aviation tools compared to simpler objects. The proposed architecture effectively reconstructs detailed and accurate digital replicas with 97% accuracy. This advancement has the potential to significantly enhance aviation training by providing immersive, interactive 3D content, bridging the gap between traditional training materials and cutting-edge digital education tools.

关键词： Training Maintenance Rendering (computer graphics) Aircraft Three-dimensional displays Lighting Image reconstruction Atmospheric modeling Accuracy Pipelines

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DeepHBSP:A Deep Learning Framework for Predicting Human Blood-Secretory Proteins Using Transfer Learning

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Journal of computer science & Technology 2021年第2期36卷 234-247页

作者： Wei Du Yu Sun Hui-Min Bao Liang Chen Ying Li Yan-Chun Liang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Computer Science College of EngineeringShantou UniversityShantou 515063China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Zhuhai 519041China

The identification of blood-secretory proteins and the detection of protein biomarkers in the blood have an important clinical application *** methods for predicting blood-secretory proteins are mainly based on traditional machine learning algorithms,and heavily rely on annotated protein *** traditional machine learning algorithms,deep learning algorithms can automatically learn better feature representations from raw data,and are expected to be more promising to predict blood-secretory *** present a novel deep learning model(DeepHBSP)combined with transfer learning by integrating a binary classification network and a ranking network to identify blood-secretory proteins from the amino acid sequence information *** loss function of DeepHBSP in the training step is designed to apply descriptive loss and compactness loss to the binary classification network and the ranking network,*** feature extraction subnetwork of DeepHBSP is composed of a multi-lane capsule ***,transfer learning is used to train a highly accurate generalized model with small samples of blood-secretory *** main contributions of this study are as follows:1)a novel deep learning architecture by integrating a binary classification network and a ranking network is proposed,superior to existing traditional machine learning algorithms and other state-of-the-art deep learning architectures for biological sequence analysis;2)the proposed model for blood-secretory protein prediction uses only amino acid sequences,overcoming the heavy dependence of existing methods on annotated protein features;3)the blood-secretory proteins predicted by our model are statistically significant compared with existing blood-based biomarkers of cancer.

关键词： blood-secretory protein deep learning capsule network transfer learning

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