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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Mahalakshmi, A. Saranya, E. Gomathi, B. Elizabeth, J. Ruby Abinaya, M. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Nehru Institute of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798350384369

The practice of getting competitive quotes from carriers to convey goods is known as shipment bidding. Currently, open bidding is handled manually, and prospective bidders are frequently disqualified from taking part in the process. The system that is being proposed is designed to record every bidding procedure and make it cumulative in terms of monetary compensation. Real-time shipping tracking, feedback mechanisms, and bid evaluation tools are just a few of the features that the system offers. Shippers choose the best transportation company after weighing bids according to criteria including cost, level of service, and experience. In order to provide safe and effective financial operations, the proposed system also supports the payment and invoicing processes. Additionally, this application has analytical tools to produce reports and insights that help carriers cut costs. © 2024 IEEE.

关键词： Competition

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SkinMarkNet: an automated approach for prediction of monkeyPox using image data augmentation with deep ensemble learning models

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Multimedia Tools and Applications 2025年第18期84卷 20177-20193页

作者： Akram, Aqsa Jamjoom, Arwa A. Innab, Nisreen Almujally, Nouf Abdullah Umer, Muhammad Alsubai, Shtwai Fimiani, Gianluca Bahawalpur Pakistan Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh Diriyah13713 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy

Monkeypox, a rare but potentially fatal viral disease, poses a significant public health challenge due to its potential for outbreaks and complications. Detecting monkeypox lesions early and accurately is vital to effectively manage and control the disease. This study introduces a novel method for classifying monkeypox lesions, employing data augmentation methods and a framework based on an ensemble of three transfer learning models called "SkinMarkNet". The dataset used in this research consists of skin lesion images collected from the Kaggle data repository, encompassing diverse demographics and lesion characteristics. This research uses image data augmentation techniques to tackle the scarcity of annotated data. This augmentation enriches the training dataset, thereby improving the model’s ability to perform effectively. Moreover, the novelty of this research work lies in the usage of three popular transfer learning models(Inception, Xception, and ResNet) for feature extraction and ensemble learning. The SkinMarkNet achieves promising results showing an accuracy of 90.615% for monkeypox lesion classification, outperforming traditional machine learning and deep learning methods utilized in recent research works. In addition, thorough comparative analysis is done with machine learning models and contemporary approaches to validate the efficacy of the proposed method. Overall, the findings underscore the potential of leveraging advanced deep learning architectures and data augmentation strategies for improving monkeypox lesion classification, thereby facilitating early diagnosis and intervention in clinical settings and public health surveillance efforts. © The Author(s), under exclusive licence to Springer science+business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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Analysis of Approaches for Identifying Key Skills in Vacancies 27

Analysis of Approaches for Identifying Key Skills in Vacanci...

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27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Korytov, Pavel V. Kholod, Ivan I. Gribetskiy, Yan Yu. Andreeva, Ekaterina A. Saint Petersburg Electrotechnical University 'LETI' Faculty of Computer Science and Technology Department of Computer Science and Engineering Saint-Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Faculty of Computer Science and Technology Department of Information Systems Saint-Petersburg Russia

ISBN: (纸本)9798350363708

The task of identifying key skills from job vacancies descriptions is considered in the article. The source data consists of vacancy texts from the 'Jobs in Russia' portal, in which skill-related entities were manually annotated. Keyword extraction methods (YAKE, PositionRank and WikiNEuRal, KBIR-InSpec), and a named entity recognition model trained on the source data are compared. © 2024 IEEE.

关键词： Extraction

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Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

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computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

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Toward an autonomous centroid-based deployment of Multi-UAV for full disaster area coverage 11

Toward an autonomous centroid-based deployment of Multi-UAV ...

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Seddiki, Manel Mouna, Mouaici Benchiba, Mahfoud Faculty of Computer Science USTHB Dept of Compter Science Systems Algiers Algeria

ISBN: (纸本)9798350369328

Multi-UAV deployment in a disaster area refers to the strategic placement of UAV s over an area affected by a natural or human-made catastrophe to carry out missions such as rescue, surveillance, inspection, or damage assessment. The primary objective in such situations is to ensure a full coverage operation to rapidly gather critical information, locate potential victims, and support disaster relief and crisis management operations, often in challenging and dangerous conditions. In this paper, we propose an autonomous deployment approach that combines a deterministic centroid-based UAV placement with a collaborative zone exploration and coverage to ensure a full coverage of the damaged area. © 2023 IEEE.

关键词： Drones

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SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system

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Neural Computing and Applications 2024年第19期36卷 11075-11095页

作者： Talaat, Fatma M. Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa Machine Learning Department Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah46537 Saudi Arabia

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan

关键词： Object recognition

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Effect of Graphite Addition on Microstructure, Mechanical Properties and Thermal Properties of Injection Molded AZ91D Alloy+1

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Materials Transactions 2024年第4期65卷 374-380页

作者： Hideshima, Yasutoshi Maeda, Fumiya Fukuta, Tadao Ozaki, Koichi Course of Advanced Systems Engineering Graduate School of Computer Science and Systems Engineering Okayama Prefectural University Soja719-1197 Japan Process Technology Development Department Technology Development Division Seiko Epson Corp. Suwa399-0211 Japan Department of Systems Engineering Faculty of Computer Science and Systems Engineering Okayama Prefectural University Soja719-1197 Japan

Magnesium chips were coated with a high concentration of graphite using a binder and were used as the raw material for injection molding. The microstructure of the magnesium injection-molded product with added graphite exhibited a dispersion of needle-like graphite particles. No significant voids were observed at the interfaces between the graphite and the matrix. The addition of more than 0.5 mass% graphite decreased the proof stress and tensile strength of the injection-molded products. The Young’s modulus of the graphite-added products tended to decrease with an increase in the graphite content, which is consistent with the lower limit of the rule of mixtures. The thermal conductivity of the 6.9 mass% graphite-added product increased compared with that of the AZ91D magnesium alloy and the coefficient of linear thermal expansion decreased. Both values are within a range that satisfied the rules of mixtures. © 2024 The Japan Institute of Metals and Materials.

关键词： Thermal expansion

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Influence of External Factors on Renewable Energy Acceptance in Indonesia 3

Influence of External Factors on Renewable Energy Acceptance...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Martono, Aris Widjaja, Andree E. Raihan, Raihan Khairunnisa, Novita Prabowo, Dimas Aditya University of Raharja Faculty of Science and Technology Tangerang Indonesia University of Pelita Harapan Faculty of Science and Technology Tangerang Indonesia Kreatif Desain Faculty of Computer Systems Tangerang Indonesia Sesindo Faculty of Digital Business Tangerang Indonesia

ISBN: (纸本)9798350367492

Indonesia, strategically positioned near the equator and within the Pacific Ring of Fire, harbours immense potential in renewable energy, including hydroelectricity, geothermal, and solar power. Despite having an estimated renewable energy capacity of 3,686 GW, current utilization remains strikingly low at 0.3%. This study assesses the government's strategies for scaling up renewable energy sectors to meet increasing energy demands sustainably and reduce reliance on fossil fuels. We analyze the expansion efforts in solar infrastructure, focusing on the implementation and impact of rooftop photovoltaic (PV) systems, alongside examining challenges in geothermal and hydropower development. Data from a renewable energy survey in Indonesia were analyzed using SPSS and Smart PLS 3.0. Out of 1200 potential responses, 1068 were processed after editing and coding. Significant barriers include the high initial costs in remote areas and the substantial financial investment required for geothermal exploration and drilling. Despite these challenges, Indonesia's approach involves stimulating investment through incentives and regulatory support for renewable projects. Effective collaboration between the public and private sectors is crucial for driving innovation, reducing costs, and broadening energy deployment. Addressing infrastructure deficiencies and enhancing technological advancements are imperative. Continued investment in research and development, supported by robust policy frameworks, will be vital for achieving sustainable energy security and reducing carbon emissions in the long term. © 2024 IEEE.

关键词： Clean energy

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A Survey on Smart Promo Creator: AI-Driven Dynamic Video Generation for Banners and Social Media Marketing 5

A Survey on Smart Promo Creator: AI-Driven Dynamic Video Gen...

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5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Pardeshi, Yashini Alai, Smital Kumbhojkar, Pranav Chaudhari, Viraj Moholkar, Kavita JSPM's Rajarshi Shahu College of Engineering Department of Computer Science and Business Systems Pune India

ISBN: (纸本)9798331518097

Generative AI (GenAI) has emerged as a transformative technology across various sectors, particularly in marketing. The paper presents an innovative solution designed to revolutionize promotional content creation for small brands with limited marketing budgets. The proposed solution aims to overcome the research gap regarding lack of affordable, automated systems for generating high-quality and personalized promotional assets. By automating the generation of high-quality banners and engaging videos, GenAI reduces reliance on manual efforts while delivering visually appealing and relevant promotional assets. Utilizing advanced machine learning algorithms such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Natural Language Processing (NLP), the proposed system can create customized banners, posters, and videos tailored to specific inputs like product images, audience demographics, seasonal themes, and events. technology streamlines the content generation process, ensures consistency across various digital advertising channels, and enhances customer engagement and brand recognition overcoming. © 2024 IEEE.

关键词： Generative adversarial networks

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