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Retraction Note: Optimizing radio access in 5G vehicle networks using novel machine learning-driven resource management

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Optical and Quantum Electronics 2024年第10期56卷 1-2页

作者： Natarajan, Rajesh Mahadev, Natesh Alfurhood, Badria Sulaiman Ranjith, Christodoss Prasanna Zaki, John Manu, M. N. Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Shinas Oman Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysore India Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Computer and Systems Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Information Science and Engineering SJBIT Bengaluru India

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Clustering the Various Categorical Data: An Exploration of Algorithms and Performance Analysis

Clustering the Various Categorical Data: An Exploration of A...

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Emerging technology (INCET), International Conference for

作者： Raman Kumar Peeta Basa Pati K. Deepa Suresh Yanan Department of Computer Science and Engineering Amrita School of Computing Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Engineering University of Technology and Applied Sciences-Al Musannah Sultanate of Oman

Clustering is a method of grouping data based on similarities, and is an unsupervised technique for discovering patterns in data. In this research paper, various clustering algorithms such as k-Means, DBSCAN, Spectral Clustering, Gaussian Mixture, and Agglomerative Clustering are compared and evaluated on Amazon Prime Video Movies and TV Shows, Netflix Movies and TV Shows, and Disney+ Movies and Tv Shows datasets. The results of the study indicate that the k-Means algorithm performed well in clustering the data for all datasets, with an overall high level of performance. Additionally, the study provides valuable insights into the genre distribution of the data, and highlights the advantages and limitations of each clustering algorithm.

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RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learning

arXiv

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arXiv 2023年

作者： Lan, Zixun Zeng, Zuo Hong, Binjie Liu, Zhenfu Ma, Fei Department of Applied Mathematics Xi’an Jiaotong-Liverpool University Suzhou China Hours Technology Co. Ltd. Suzhou China Department of Information and Computing Science Xi’an Jiaotong-Liverpool University Suzhou China

The reaction center consists of atoms in the product whose local properties are not identical to the corresponding atoms in the reactants. Prior studies on reaction center identification are mainly on semi-templated retrosynthesis methods. Moreover, they are limited to single reaction center identification. However, many reaction centers are comprised of multiple bonds or atoms in reality. We refer to it as the multiple reaction center. This paper presents RCsearcher, a unified framework for single and multiple reaction center identification that combines the advantages of the graph neural network and deep reinforcement learning. The critical insight in this framework is that the single or multiple reaction center must be a node-induced subgraph of the molecular product graph. At each step, it considers choosing one node in the molecular product graph and adding it to the explored node-induced subgraph as an action. Comprehensive experiments demonstrate that RCsearcher consistently outperforms other baselines and can extrapolate the reaction center patterns that have not appeared in the training set. Ablation experiments verify the effectiveness of individual components, including the beam search and one-hop constraint of action space. Copyright © 2023, The Authors. All rights reserved.

关键词： Atoms

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Two-stage CNN-based framework for leukocytes classification

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Computers in Biology and Medicine 2025年 187卷 109616-109616页

作者： Khan, Siraj Sajjad, Muhammad Escorcia-Gutierrez, José Dhahbi, Sami Hijji, Mohammad Muhammad, Khan Department of Computer Science Islamia College University Peshawar25120 Pakistan Department of Computational Science and Electronics Universidad de la Costa CUC Barranquilla080002 Colombia Applied College of Mahail Aseer King Khalid University Muhayil Aseer62529 Saudi Arabia Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Leukocytes are pivotal markers in health, crucial for diagnosing diseases like malaria and viral infections. Peripheral blood smear tests provide pathologists with vital insights into various medical conditions. Manual leukocyte counting is challenging and error-prone due to their complex structure. Accurate segmentation and classification of leukocytes remain challenging, impacting both accuracy and efficiency in blood microscopic image analysis. To overcome these limitations, we propose a robust two-stage CNN framework that integrates YOLOv8 for precise segmentation and MobileNetV3 for effective classification. Initially, WBCs are segmented using YOLOv8m-seg, extracting ROIs for subsequent analysis. Then, features from segmented ROIs are used to train MobileNetV3, classifying WBCs into lymphocytes, monocytes, basophils, eosinophils, and neutrophils. This framework significantly advances leukocyte categorization, enhancing diagnostic performance and patient outcomes. The proposed technique achieved impressive accuracy rates of 99.56 %, 99.19 % and 98.89 % during segmentation and 99.28 %, 99.63 % and 98.49 % during classification on Raabin-WBC, PBC and LISC datasets, respectively, outperforming state-of-the-art methods. © 2024 Elsevier Ltd

关键词： White blood cells Blood smear images Image segmentation Deep learning MobileNetV3 YOLOv8 Image classification

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Offline Writer Identification using Deep Convolution Neural Network 2

Offline Writer Identification using Deep Convolution Neural ...

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2022

作者： Durou, Amal M. Aref, Ibrahim A. Erateb, Suleiman El-Mihoub, Tarek A. Ghalut, Tarik Emhemmed, Adel Saad University of Tripoli Faculty of Information Technology Tripoli Libya Lancaster University School of Computing and Comms Lancaster United Kingdom Coventry University Engineering and Digital Technologies Coventry United Kingdom Jade University of Applied Sciences Department of Engineering Science Wilhelmshaven Germany University of Tripoli Electrical Engineering Department Tripoli Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

Deep convolutional neural networks (DCNN) are efficient in solving different pattern recognition problems and have been applied to extract image features (IFs). This paper investigates using deep learning (DL) techniques to improve the performance of the writer identification (WI) process. This work presents a novel approach for WI tasks by combining a DL technique with machine learning (ML). A convolutional neural network (CNN) is employed as a feature extractor along with a ML algorithm to classify those features. The standard Alex-Net model is utilized to extract IFs that located in the fully connected layers (FCLs). The support vector machine (SVM) model is selected as the classifier due to its efficient capabilities to improve identification performance (IP). The proposed model is tested using various types of the datasets, namely the Islamic Heritage Project (IHP) and Clusius. Furthermore, IAM and ICFHR-2012 datasets have been employed for benchmarking the proposed model. The results demonstrate the model achieves superior performance. © 2022 IEEE.

关键词： Convolutional neural networks

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Visual Object Tracking Using Machine Learning 1st

Visual Object Tracking Using Machine Learning

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Science, Engineering Management and Information technology First International Conference, SEMIT 2022

作者： Odeh, Ammar Keshta, Ismail Al-Fayoumi, Mustafa Department of Computer Science King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Amman Jordan Department of Computer Science College of Applied Sciences AlMaarefa University Riyadh Riyadh Saudi Arabia

ISBN: (纸本)9783031403972

Visual object tracking has become a very active research area in recent years. Each year, a growing number of tracking algorithms are proposed. Object detection and tracking is a critical and challenging task in many critical computer vision applications, including automated video surveillance, traffic monitoring, autonomous robot navigation, and intelligent environments. Object tracking is segmenting an object of interest and tracking its velocity, orientation, and occlusion in a video scene to extract useful information. Over the last two decades, several object tracking approaches have been developed to design a robust object tracker that covers all practical obstacles in real-world operations. This paper reviews recent trends and advances in tracking and assesses the reliability of various trackers based on feature extraction techniques. In video processing, visual tracking has a wide range of applications. When a target is identified in one video frame, it is frequently advantageous to track that object in subsequent frames. Every successful frame in which the target is tracked yields more information about the target’s identity and activity. Because tracking is more straightforward than detection, tracking algorithms can require fewer computational resources than object detectors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object recognition

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NLP-Based Sentiment Analysis using Deep Learning Methods

NLP-Based Sentiment Analysis using Deep Learning Methods

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Advances in computing, Communication and Networking (ICAC2N), International Conference on

作者： Dheeraj Kumar Sandeep Bhatia Hardeep Singh Dhillon Amit Kumar Goel School of Computing Science and Engineering Galgotias University Greater Noida Department of Applied Sciences Chandigarh Engineering College (CEC) Chandigarh Group of Colleges Mohali Panjab School of Engineering and Technology Apeejay Stya University Sohna Gurugram India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Opinion mining systems rely heavily on sentiment analysis because to the vast amount of data and opinions that are generated, exchanged, and sent on a regular basis through the internet and other media. This study presents a deep learning network-based sentiment analysis categorization that was created and compares the results across multiple deep learning networks. Using the Multilayer Perceptron, a benchmark for other networks' performance was established (MLP). Built and implemented on the 50K movie review file IMDB dataset were a hybrid model of long short-term memory (LSTM) and convolutional neural network (CNN), as well as an LSTM recurrent neural network. Reviews for the dataset were 50% positive and 50% negative. After Word2Vec performed its initial pre-processing on the data, word embedding was applied. The hybrid CNN_LSTM model outperformed the MLP and the separate CNN and LSTM networks, as seen by the findings. While CNN reported an accuracy of 88.9%, MLP and LSTM reported accuracy of 87.78% and 87.68, respectively. 89.4% accuracy was reported by CNN_LSTM. Results further show that the proposed deep learning models have performed better than SVM, Naïve Bayes, and RNTN models based on English datasets presented in previous works.

关键词： Deep learning Sentiment analysis Analytical models Accuracy Recurrent neural networks Reviews Computational modeling Data models Convolutional neural networks Long short term memory

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Markov chain quasi-Monte Carlo method for forecasting fire hotspots in Sarawak, Malaysia

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Environmental Science and Pollution Research 2024年第35期31卷 48608-48619页

作者： Zakaria, Nurul Nnadiah Daud, Hanita Sokkalingam, Rajalingam Othman, Mahmod Abdul Kadir, Evizal Mohd Aris, Muhammad Naeim Muhammad, Noryanti Maharani, Warih Fundamental and Applied Sciences Department Universiti Teknologi PETRONAS Perak Darul Ridzuan Bandar Seri Iskandar32610 Malaysia Information System Department Universitas Islam Indragiri Tembilahan29212 Indonesia Informatics Engineering Department Universitas Islam Riau Pekanbaru28284 Indonesia Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Darul Ehsan Bangi43600 Malaysia Centre of Excellence in Artificial Intelligence & amp Data Science Universiti Malaysia Pahang Al-Sultan Abdullah Lebuh Persiaran Tun Khalil Yaakob Pahang Darul Makmur Kuantan26300 Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Lebuh Persiaran Tun Khalil Yaakob Pahang Darul Makmur Kuantan26300 Malaysia School of Computing Telkom University Bandung40257 Indonesia

Stochastic modeling approaches have attracted many researchers to the field. However, fire hotspot detection suffers from not using a Markov chain quasi-Monte Carlo (MCQMC) as a forecasting methodology. This paper proposes improvements to the computational time by combining the strengths of the Markov chain Monte Carlo (MCMC) and quasi-Monte Carlo (QMC) methods. The proposed method can lead to more precise and stable results, particularly in problems with high-dimensional integration or complex probability distributions. The proposed method is applied to a case study of fire hotspot detection in Sarawak, Malaysia. The outcome of this study reveals that the MCQMC method is more computationally efficient, taking only 0.0746 seconds compared to MCMC’s 0.0914 seconds and QMC’s 0.0994 seconds. It is shown that the best option derived by the proposed method is effective in predicting fire hotspots and providing quick solutions to protect the environment and communities from forest fires. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Fires

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On the Perception of Small Sub-graphs

arXiv

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arXiv 2023年

作者： Miller, Jacob Ghoniem, Mohammad Wu, Hsiang-Yun Purchase, Helen C. Department of Computer Science University of Arizona United States Luxembourg Institute of Science and Technology Luxembourg St. Pölten University of Applied Sciences Austria Department of Human-Centred Computing University of Monash Australia

Interpreting a node-link graph is enhanced if similar sub-graphs (or ‘motifs’) are depicted in a similar manner – that is, they have the same visual form. Small motifs within graphs may be perceived to be identical when they are structurally dissimilar, or may be perceived to be dissimilar when they are identical. This issue primarily relates to the Gestalt principle of similarity, but may also include an element of quick, low-level pattern-matching. We believe that if motifs are identical, they should be depicted identically;if they are nearly-identical, they should be depicted nearly-identically. This principle is particularly important in domains where motifs hold meaning and where their identification is important. We identified five small motifs: bi-cliques, cliques, cycles, double-cycles, and stars. For each, we defined visual variations on two dimensions – same or different structure, same or different shape. We conducted a crowd-sourced empirical study to test the perception of similarity of these varied motifs, and found that determining whether motifs are identical or similar is affected by both shape and structure. © 2023, CC BY.

关键词： Pattern matching

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AI-based Wearable Vision Assistance System for the Visually Impaired: Integrating Real-Time Object Recognition and Contextual Understanding Using Large Vision-Language Models

arXiv

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arXiv 2024年

作者： Baig, Mirza Samad Ahmed Gillani, Syeda Anshrah Shah, Shahid Munir Aljawarneh, Mahmoud Khan, Abdul Akbar Siddiqui, Muhammad Hamzah Department of Computing Faculty of Engineering Science and Technology Hamdard University Karachi Pakistan Applied Science Private University Amman Jordan Argaam Investment Riyadh Saudi Arabia Danat Fz LLC Karachi Pakistan Doaz Korea Republic of

Visual impairment affects the ability of people to live a life like normal people. Such people face challenges in performing activities of daily living, such as reading, writing, traveling and participating in social gatherings. Many traditional approaches are available to help visually impaired people;however, these are limited in obtaining contextually rich environmental information necessary for independent living. In order to overcome this limitation, this paper introduces a novel wearable vision assistance system that has a hat-mounted camera connected to a Raspberry Pi 4 Model B (8GB RAM) with artificial intelligence (AI) technology to deliver real-time feedback to a user through a sound beep mechanism. The key features of this system include a user-friendly procedure for the recognition of new people or objects through a one-click process that allows users to add data on new individuals and objects for later detection, enhancing the accuracy of the recognition over time. The system provides detailed descriptions of objects in the user’s environment using a large vision language model (LVLM). In addition, it incorporates a distance sensor that activates a beeping sound using a buzzer as soon as the user is about to collide with an object, helping to ensure safety while navigating their environment. A comprehensive evaluation is carried out to evaluate the proposed AI-based solution against traditional support techniques. Comparative analysis shows that the proposed solution with its innovative combination of hardware and AI (including LVLMs with IoT), is a significant advancement in assistive technology that aims to solve the major issues faced by the community of visually impaired people. © 2024, CC BY.

关键词： Assistive technology

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