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Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： Software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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A Comprehensive Systematic Review: Advancements in Skin Cancer Classification and Segmentation Using the ISIC Dataset

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computer Modeling in Engineering & sciences 2024年第9期140卷 2131-2164页

作者： Madiha Hameed Aneela Zameer Muhammad Asif Zahoor Raja Department of Computer Sciences Pakistan Institute of Engineering and Applied SciencesNilore50700Islamabad Future Technology Research Center National Yunlin University of Science and TechnologyDouliou64002TaiwanChina Directorate of IT Services University of GujratGujrat50700Pakistan

The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer *** datasets contain tens of thousands of dermoscopic photographs,each accompanied by gold-standard lesion diagnosis *** challenges associated with ISIC datasets have spurred significant advancements,with research papers reporting metrics surpassing those of human *** cancers are categorized into melanoma and non-melanoma types,with melanoma posing a greater threat due to its rapid potential for metastasis if left *** paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images,processes historically known for their *** notable advancements in machine learning and deep learning models,persistent challenges remain,largely due to the intricate nature of skin lesion *** review research on convolutional neural networks(CNNs)in skin cancer classification and segmentation,identifying issues like data duplication and augmentation *** explore the efficacy of Vision Transformers(ViTs)in overcoming these challenges within ISIC dataset *** leverage their capabilities to capture both global and local relationships within images,reducing data duplication and enhancing model ***,ViTs alleviate augmentation issues by effectively leveraging original *** a thorough examination of ViT-based methodologies,we illustrate their pivotal role in enhancing ISIC image classification and *** study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological ***,this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies,highlighting their significance in improving algorithmic

关键词： Medical image skin cancer classification skin cancer segmentation international skin imaging collaboration convolutional neural network deep learning

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Enhancing Event Extraction and Impact Analysis with Knowledge Graph Augmented Transformer 21

Enhancing Event Extraction and Impact Analysis with Knowledg...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Songklang, Kallaya Srisomboon, Kanabadee Lee, Wilaiporn Prayote, Akara King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9798350377088

Understanding the impact of events reported in news articles plays pivotal roles in various tasks. In this study, we propose a framework for extracting event components from the news and analyzing their multifaceted impact. The framework employs transformers techniques for event extraction and leverages a biography knowledge base to assess the potential impact of events across social, political, economic, and culture (SPEC) dimensions. Our framework achieves an F1-score of 78.12% in event identification as well as significant accuracy score in evaluating the impact levels prediction. By integrating results from event extraction and impact analysis, the level of multifaceted influence of the event along with the event information are effectively visualized. This comprehensive approach enhances understanding of multi-perspective interpretation and supports insights for various applications. © 2024 IEEE.

关键词： Distribution transformers

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Automatic Speech Recognition Techniques for Transcription of Thai Traditional Medicine Texts 21

Automatic Speech Recognition Techniques for Transcription of...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Popun, Jettasic Lee, Wilaiporn Prayote, Akara King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9798350377088

Currently, Automatic Speech Recognition (ASR) technology is widely used for communication convenience in converting speech to text. Initial surveys reveal that existing studies on ASR, both in Thai and foreign languages, focus on contemporary language communication, lacking support for specialized vocabulary. Traditional Thai Medicine contain ancient medical terminology recorded in various media such as palm leaves, parchment, notebooks, and inscriptions. Centuries of textual preservation have led to text degradation. Given the suitability of ASR study to accommodate medical terminology and ancient languages, it is proposed to apply it to convert original Traditional Thai Medicine texts into contemporary language, aiding experts in reading and interpreting them. ASR involves converting speech signals into text. In this study, Hidden Markov Model techniques using Kaldi are employed. Due to the absence of a Thai Traditional Medicine vocabulary repository, a dataset comprising text and speech is developed, consisting of 900 training sentences and 100 test sentences randomly selected from recordings of 10 Thai Traditional Medicine experts. Offline evaluation methods are employed, resulting in improved accuracy rates. Future efforts will focus on expanding the speech dataset to enhance recognition accuracy further. This study lays the groundwork for efficiently extracting knowledge from Traditional Thai Medicine prescriptions, ensuring the preservation of this valuable heritage for future generations. © 2024 IEEE.

关键词： Hidden Markov models

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Unseen Named Entity Recognition for Thai Language 21

Unseen Named Entity Recognition for Thai Language

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Kuntichod, Watcharet Lee, Wilaiporn Prayote, Akara King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9798350377088

Most common challenges in Named-entity recognition (NER) require suitable and annotated datasets, tailored to the specific domain of the documents under analysis, for example, legal, medical, and financial documents each require distinct datasets. Reliable identification of named entities within the training set is crucial. Annotations is a vital role in building an effective mechanism. However, creating training sets demands significant human effort, contributing to the high costs associated with NER. High quality training materials are indispensable for evolving machine-learning algorithms, underscoring the importance of dependable training data. Thus, the primary challenge lies in acquiring and managing data rather than in the algorithms themselves. To overcome most common situations of finding new unseen names, this paper proposes a mechanism to detect a new undiscovered name in Thai language based on sentence contexts. The method uses BI-LSTM model to learn sentence structures and predict a new unseen name from its context. Our framework comprises two main tasks. The first one is word entity extraction. The second task is using the model to identify unrecognized named entities. Evaluation of the proposed method is described with a successful rate of accuracy in discovering newly identified entities. This contributes to the expansion of entity recognition capabilities in Thai language processing. © 2024 IEEE.

关键词： BI-LSTM Herb Entity Unseen Named Entity

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Cross-Modality Disentangled Information Bottleneck Strategy for Multimodal Sentiment Analysis

Cross-Modality Disentangled Information Bottleneck Strategy ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Deng, Zhengnan Huang, Guoheng Zhong, Guo Yuan, Xiaochen Huang, Lian Pun, Chi-Man School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou510420 China Macao Polytechnic University Faculty of Applied Sciences 999078 China Guangdong Mechanical and Electrical College Department of Applied Electronics Guangzhou510515 China University of Macau Department of Computer and Information Science China

ISBN: (纸本)9781665410205

Multimodal Sentiment Analysis (MSA) has been a pivotal domain in current research area which utilizes diverse information carriers such as videos containing multiple modal-ities to understand the user's sentiment. With the success of multimodal fusion techniques, lots of fusion strategies have been proposed to obtain a favorable multimodal joint representation for MSA. However, existing studies hardly consider the problem of redundant information in unimodal, resulting in the joint representation may contain much redundant information from different modalities, thus limiting the accuracy of sentiment prediction. In this work, we propose a Cross-Modality Disentangled Information Bottleneck Strategy (CMDIBS), which consists of a Cross-Modality Knowledge Awareness (CMKA) module and a Multimodal Disentangled Information Bottleneck (MDIB) mechanism. Specifically, the CMKA module encourages in-teractions among different modalities to learn the sentiment embedding relevant to the predicted goals. In particular, MDIB mechanism aims to maximize the mutual information (MI) between the multimodal joint representation and the predicted label, and maximize the MI between the style embedding with the label and the input data while constraining the MI between the multimodal joint representation and the style embedding to obtain a succinct and efficient multimodal joint representation. Experimental results on the benchmark datasets, namely CMU-MOSI and CMU-MOSEI, indicated that the proposed method surpasses existing approaches and attains SOTA performance. © 2024 IEEE.

关键词： Benchmarking

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Meta-Learning-Driven QoS Estimation for Self-Optimizing 6G Networks in Stochastic α-η-μ Fading Channels with Multi-Layer Selection Combining and Cross-Layer Interference Adaptation 2

Meta-Learning-Driven QoS Estimation for Self-Optimizing 6G N...

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2nd International Conference on IT Innovations and Knowledge Discovery, ITIKD 2024

作者： Kaushik, Keshav Gaur, Priyanka Mujahid, Barga Mohammed Khan, Mudassir Mustafa, Elfatih Elmubarak Singh, Amrita Sharda School of Computer Science & Engineering Sharda University Greater Noida India India Applied College Khamish Mushaiyt Department of Computer Science King Khalid University Saudi Arabia College of Computer Science Applied College Tanumah King Khalid University Department of Computer Science Abha Saudi Arabia College of Computer Science and Information Technology University of Bisha Department of Computer Science and Artificial Intelligence Saudi Arabia Chandigarh Engineering College Department of Computer Science and Engineering Chandigarh Group of Colleges Mohali Punjab Jhanjeri India

ISBN: (纸本)9798350355468

The authors proposed to use a meta-learningbased approach for QoS optimization in future 6G networks that are self-optimizing sections, focusing on stochastic α - η - μ fading channels. The proposed model utilizes information learned in the past so that it can rapidly adapt to these changes as they arrive, providing much better area adaptation, energy efficiency and interference mitigation than traditional learning approaches. You push the best and QoS, network throughput, latency abased simulation results are provided under high mobility in addition to severe fading conditions. The metalearning model showed accelerated convergence, adaptive resource allocation and improved interference mitigation which naturally led to QoS improvement up to 80% as well energy efficiency increased up to 65-90%, network reliability were enhanced. This step helps build green and robust communication mechanisms in the next-generation networks. © 2025 IEEE.

关键词： Fading channels

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Survey of Crop Identification Using Hyperspectral Satellite Images 1

Survey of Crop Identification Using Hyperspectral Satellite ...

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Premakar, Monica Doss, Srinath Hiran, Kamal Kant Azteca University Department of Computer Science and IT Mexico United States Botho University Faculty of Engineering and Technology Botswana Symbiosis University of Applied Sciences School of Computer Science and IT Indore India

ISBN: (纸本)9798350335774

Hyperspectral images have a lot of spatial and spectral information, which makes them great for fine crop classification and detection. The hyperspectral remote sensing is more capable than panchromatic remote sensing of obtaining the spectral characteristics and their differences with greater precision. One of the most crucial methods for classifying crops is to make use of remote sensing technology to advance agricultural development. This survey is focusing on the review of various techniques implemented in 25 research papers. The crop identification performance metrics, techniques, published year, dataset used, and tools used are reviewed in this survey. Also, the research gaps and issues in the existing researches are discussed, which offers the ideas for developing new techniques for crop identification. © 2023 IEEE.

关键词： Classification (of information)

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MANA Cryptocurrency: Transactional Dynamics & Financial Metrics Study

MANA Cryptocurrency: Transactional Dynamics & Financial Metr...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Kumar, Amit Sharma, Neha Chauhan, Rahul Joshi, Kireet Raj, Ankush Iqbal, Mohammed Ismail Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India Graphic Era Hill University Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Computer Science & Engineering Uttarakhand Dehradun248002 India University of Jammu Department of Computer Science and IT India University of Technology and Applied Sciences Petroleum Engineering Department Nizwa Oman

ISBN: (纸本)9798350394412

This research investigates the emerging domain of the Metaverse, a digital realm that is significantly influencing social interactions and economic landscapes. This study aims to investigate the complex variables that contribute to the economic valuation of virtual properties on the Decentraland platform, which is a famous Metaverse platform. Despite the growing significance of the Metaverse and its status as a prominent economic force, there is a noticeable lack of empirical research examining the distinct dynamics of land valuation within this context. The current corpus of scholarly work mostly focuses on valuations in real-world contexts, resulting in a significant knowledge gap about the specific factors that influence land prices in the Metaverse. This research seeks to address the existing vacuum in knowledge by doing a comprehensive literature review and platform analysis. The primary objective is to provide insights into the various elements that influence the financial prospects of Metaverse lands. The anticipated results of this study are expected to provide significant contributions to the comprehension of Metaverse economics. Additionally, these findings will serve as a foundation for future research endeavors that aim to explore the convergence of virtual and physical realms in relation to financial value. © 2024 IEEE.

关键词： Decentralized finance

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Real-Time Facial Recognition System for Secure College Bus Transport using Deep Learning Techniques 3

Real-Time Facial Recognition System for Secure College Bus T...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, M.N. Saranya, S.S. Pragatheeswari, E. Priya, N. Kaviya Ranjith, S.R. Vanchivel, N. Rahul Kongu Engineering College Department of Computer Science and Design Tamil Nadu India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India Kongu Engineering College Department of Csd Tamil Nadu India

ISBN: (纸本)9798331509675

The proposed system aims to enhance student transportation security through real-time face detection and recognition. Leveraging the MTCNN framework for accurate face detection and the FaceNet model for reliable face recognition, the system ensures only authorized students can board designated buses by comparing captured faces with a pre-registered database. In case of mismatches, alerts are triggered to notify drivers and authorities. Additionally, parallel implementation of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers improves authentication accuracy. The system's deep learning-based architecture ensures robust performance under varying environmental conditions and supports continuous learning for long-term reliability. © 2025 IEEE.

关键词： Students

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