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Sand Cat Swarm Optimization with Deep Transfer Learning for Skin Cancer Classification

computer Systems science & engineering 2023年第11期47卷 2079-2095页

作者： C.S.S.Anupama Saud Yonbawi G.Jose Moses E.Laxmi Lydia Seifedine Kadry Jungeun Kim Department of Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Skin cancer is one of the most dangerous *** of the high melanoma death rate,skin cancer is divided into non-melanoma and *** dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin ***,pathology and biopsy examinations are required for cancer *** studies have formulated computer-based systems for detecting skin cancer from skin lesion *** recent advancements in hardware and software technologies,deep learning(DL)has developed as a potential technique for feature ***,this study develops a new sand cat swarm optimization with a deep transfer learning method for skin cancer detection and classification(SCSODTL-SCC)*** major intention of the SCSODTL-SCC model lies in the recognition and classification of different types of skin cancer on dermoscopic ***,Dull razor approach-related hair removal and median filtering-based noise elimination are ***,the U2Net segmentation approach is employed for detecting infected lesion regions in dermoscopic ***,the NASNetLarge-based feature extractor with a hybrid deep belief network(DBN)model is used for ***,the classification performance can be improved by the SCSO algorithm for the hyperparameter tuning process,showing the novelty of the *** simulation values of the SCSODTL-SCC model are scrutinized on the benchmark skin lesion *** comparative results assured that the SCSODTL-SCC model had shown maximum skin cancer classification performance in different measures.

关键词： Deep learning skin cancer dermoscopic images sand cat swarm optimization machine learning

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Success Factors of Enterprise Resource Planning: A State-of-the-Art Mini Review

Success Factors of Enterprise Resource Planning: A State-of-...

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2024 International Conference on Artificial Intelligence, Metaverse and Cybersecurity, ICAMAC 2024

作者： BaniMustafa, Ahmed Bulkrock, Olla Faculty of Computer Information Science Higher Colleges of Technology Abu Dhabi United Arab Emirates Software Engineering Department Princess Sumaya University for Technology Amman Jordan

ISBN: (纸本)9798350353488

Enterprise Resource Planning Systems (ERP) are vital for today’s businesses. However, the successful implementation of ERP systems faces several challenges, which can determine its success or failure. This paper provides a brief state-of-the-art review of the success factors of ERP implementation using various applications that span different contexts. The paper discusses and compares the aims defined and the techniques applied in these studies, as well as the data used and the results obtained from each of these studies. This review paper reviews published work to tackle these challenges, covering the literature published in 2020-2023. A special effort was also made to focus on high-quality papers published in top journals with high-impact factors and indexed in Scopus and Web of science. The review process is repeatable, as special keywords were used to search the literature using publishers’ databases and three specialized tools. The paper concludes by discussing the future of the successful implementation of ERP systems. The paper argues that ERP systems will continue to have a major impact on the success of today’s businesses. Therefore, they must carefully consider various factors that influence their successful implementation. © 2024 IEEE.

关键词： information use

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Cyber security skills development in South Africa: Addressing the Gender Gap in the Industry 8

Cyber security skills development in South Africa: Addressin...

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8th Conference on information Communication Technology and Society, ICTAS 2024

作者： Ramonyai, Tebogo Motlatso Mpekoa, Noluntu Tom, Sheethal University of Johannesburg Academy of Computer Science & Software Engineering Cape Town South Africa IIE Varsity College School of Information Technology Cape Town South Africa

ISBN: (纸本)9798350314892

This paper investigates the gender gap in South Africa's cybersecurity sector and its effects on both the sector and the nation's economic growth. There is a significant shortage of skilled workers in the sector, and women are notably underrepresented. The main research question examines how the gender gap affects the industry's development, innovation, and national security posture, emphasizing the importance of utilizing the nation's talent pool to its fullest capacity to combat the evolving cyber threats. Insights and perspectives on the gender disparity in the cybersecurity sector were gathered through a questionnaire circulated amongst cybersecurity professionals and academics. These participants were chosen due to their knowledge and direct engagement in the subject, which offers valuable insights into the obstacles that women encounter in their workplace. The study highlights a number of problems, including gender stereotypes, a lack of access to education and training, and a non-inclusive workplace culture, that contribute to the gender gap. The paper makes the case that in order to solve these problems and advance diversity and inclusivity in the workplace, all stakeholders must work together to close the gender gap. In order to overcome the gender gap in cybersecurity in South Africa, the study suggests special programs aimed at expanding access to education and training opportunities, promoting gender diversity and inclusivity, and creating a more hospitable and equal work environment. This will boost the country's economic growth and strengthen security against cyberattacks. © 2024 IEEE.

关键词： Cybersecurity

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CNN channel pruning via gradient integration and knowledge distillation 17

CNN channel pruning via gradient integration and knowledge d...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Lian, Suyun Zhao, Yang Cai, Jiajian Pei, Jihong Shenzhen University The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

ISBN: (纸本)9798350387384

With the advancement of artificial intelligence technology, intelligent applications have permeated all aspects of human life, including automatic driving, intelligent security, and face recognition. Convolutional neural networks (CNNs) are essential models for AI, but their complexity increases with more available data. The complex models are unsuitable for edge devices like wearables and smartphones due to high memory and computational requirements. In this paper, the compact model is achieved by pruning some redundant output channels. To effectively measure the validity of the output channels, this paper proposes a channel pruning algorithm based on gradient integration, which combines the gradient of the channel with the model output and the channel's values. This pruning could destroy model accuracy, so knowledge distillation is introduced during fine-tuning to leverage the original model's reasoning ability. Experiments show that our approach improves accuracy by 0.51% while reducing parameters by 30.1% and FLOPs by 26% in the VGG16 model. © 2024 IEEE.

关键词： Convolutional neural networks

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A Transformer-based method for non-contact heart rate estimation from facial videos recorded in realistic environment 3

A Transformer-based method for non-contact heart rate estima...

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3rd Vision-Based Remote Physiological Signal Sensing Challenge and Workshop, RePSS 2024

作者： Hu, Yi Liu, Xuenan Rao, Xinlong Li, Bojing School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China School of Software Hefei University of Technology Anhui Hefei230601 China

The Video Photoplethysmography (VPPG) technique, while increasingly popular due to its convenience and cost-effectiveness, faces challenges in handling continuous head movements and vigorous motion interferences encountered in real-life scenarios. In this paper, we present a Transformer-based approach aimed at enhancing the robustness of heart rate estimation from facial videos. Leveraging the selfattention mechanism inherent in Transformers, our method adeptly captures both temporal dependencies and spatial information, thereby elevating the accuracy and resilience of heart rate estimation, even in challenging conditions. Through extensive experiments conducted on real-world face video datasets, we illustrate the effectiveness of our approach. Our results demonstrate significant improvements over existing methods in mitigating motion artifacts and enhancing the reliability of non-contact heart rate estimation in practical environments. © 2024 Copyright for this paper by its authors.

关键词： heart rate detection non-contact type Transformer Video Photoplethysmography

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An Acceptance Model of Using Mobile-Government Services(AMGS)

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computer Modeling in engineering & sciences 2022年第5期131卷 865-880页

作者： Ahmad Althunibat Mohammad Abdallah Mohammed Amin Almaiah Nour Alabwaini Thamer Ahmad Alrawashdeh Department of Software Engineering Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmmanJordan Faculty of Computer Sciences and Information Technology King Faisal UniversityAl-AhsaSaudi Arabia

In recent years,the telecommunications sector is no longer limited to traditional communications,but has become the backbone for the use of data,content and digital applications by individuals,governments and companies to ensure the continuation of economic and social activity in light of social distancing and total closure inmost countries in the ***,electronic government(e-Government)andmobile government(m-Government)are the results of technological evolution and ***,it is important to investigate the factors that influence the intention to use m-Government services among Jordan’s *** paper proposed a new m-Government acceptance model in Jordan(AMGS);this model combines the information System(IS)Success Factor Model and Hofstede Cultural Dimensions *** study was conducted by surveying different groups of the Jordanian *** questionnaire was used to collect data from203 *** regression analysis has been conducted to analyze the *** results indicate that the significant predictors of citizen intention to use m-Government services in Jordan are information Quality,Service Quality,Uncertainty Avoidance,and Indulgence ***,the results also suggest that Power Distance is not a significant predictor of citizen intention to use m-Government services.

关键词： Acceptance test e-Government m-Government acceptance model IS success factor model Hofstede cultural dimensions theory

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MOFC: Network Embedding Based on Multi-Orders Friends Contribution for User Identification 6

MOFC: Network Embedding Based on Multi-Orders Friends Contri...

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6th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2023

作者： Zhang, Jiajing Xu, Neng Chen, Jinlan Ma, Tinghuai School of Electronics and Information Engineering Anhui Jianzhu University China School of Mechanical and Electrical Engineering Anhui Jianzhu University China School of Computer and Software Nanjing University of Information Science & Technology China

ISBN: (纸本)9798400709203

Different social networks are independent of each other, and identifying the same person in different social networks is of great significance for cross-network information dissemination and the construction of a complete portrait of users. Existing methods for user identification via network embedding ignore the different contributions of neighbors and only consider low-order friend relationships. Aiming at these shortcomings, an algorithm of Network Embedding based on Multi-Orders Friends Contribution for User Identification (MOFC) is proposed. Firstly, the difference contribution degree of nodes to their friends is used to assign different weights to the edges between the nodes and their friends. Secondly, the embedding representation vectors of multi-orders friend contribution are used to describe the different orders friend relations of the nodes, and then the representation vectors of different orders are merged to more comprehensively represent network features. Finally, the fusion representation vectors are used as input sample of a neural network to train and build a user identification model. Experiments show that the evaluation indicators of the model have been significantly improved compared with several advanced methods. Compared with the newer and better the CSN_LINE, the accuracy rate of MOFC can be increased by 4.83%, the precision rate can be increased by 6.77%, the recall rate can be increased by 5.49%, the F1-score can be increased by 5.43%. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Network embeddings

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CEMA-LSTM:Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

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computer Modeling in engineering & sciences 2023年第4期135卷 45-64页

作者： Zhiyun Yang Qi Liu HaoWu Xiaodong Liu Yonghong Zhang School of Computer and Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK School of Automation Nanjing University of Information Science TechnologyNanjing210044China

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy *** relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation,where radar echo maps were used to predict their consequent moment,so as to recognize potential severe convective weather ***,these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation,due to the size limitation of convolution filter,lack of global feature,and less attention to features from previous *** address the problems,this paper proposes a CEMA-LSTM recurrent unit,which is embedded with a Contextual Feature Correlation Enhancement Block(CEB)and a Multi-Attention Mechanism Block(MAB).The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction;the MAB uses a position and channel attention mechanism to capture global features of radar *** practical radar echo datasets were used involving the FREM and CIKM 2017 *** quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTMover recentmodels,e.g.,PhyDNet,MIM and PredRNN++,*** particular,compared with the second-rankedmodel,its average POD,FAR and CSI have been improved by 3.87%,1.65%and 1.79%,respectively on the FREM,and by 1.42%,5.60%and 3.16%,respectively on the CIKM 2017.

关键词： Radar echo extrapolation attention mechanism long short-term memory deep learning

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Enhancing Soft Skills in Autistic Children: The Next Generation of Mind Champ's Technological Approach

Enhancing Soft Skills in Autistic Children: The Next Generat...

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Advancements in Computing (ICAC), International Conference on

作者： Kalna Peiris Ruchira Nelaka Kalpani Manathunga Department of Computer Systems Engineering Department of Information Technology Department of Computer Science and Software Engineering

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Autism Spectrum Disorder (ASD) significantly impacts a child's ability to navigate social interactions, regulate emotions, and develop adaptive skills crucial for daily functioning. While various interventions exist to address cognitive and academic skills, the development of soft skills such as communication, emotional regulation, social interaction, and creativity remains an under explored area. This paper builds upon the foundational work of the Mind Champ platform, which originally targeted both learning and soft skills, by focusing exclusively on enhancing the soft skills development of autistic children. This enhanced version of the Mind Champ platform leverages advanced behavioral and emotional analysis techniques to offer a comprehensive technological solution. Through interactive activities centered on painting and music, the platform creates a structured, engaging, and supportive environment tailored to the unique needs of children with ASD. By prioritizing emotional engagement and creative expression, the platform empowers children to improve their social abilities, emotional regulation, and adaptability. The results from our continued research indicate that these technology-driven interventions contribute significantly to the holistic development of soft skills in autistic children, providing them with valuable tools to navigate social environments more effectively.

关键词： Autism Navigation Variable speed drives Motors Regulation Real-time systems Creativity Next generation networking Painting Resilience

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Performance Analysis of Tesseract and EasyOCR for Bangla Optical Character Recognition on the Novel Bangla CrossHair Dataset 3

Performance Analysis of Tesseract and EasyOCR for Bangla Opt...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Chowdhury, Abdulla Nasir Sami, Aftar Ahmad Mamun, Shah Masud Parvej Absar, Shakib Rahman, Fuad Kohinoor, Saidur Rahman Leading University Department of Electrical and Electronic Engineering Sylhet Bangladesh Leading University Department of Computer Science and Engineering Sylhet Bangladesh Taylor's University Department of Software Engineering Kualalampur Malaysia King Fahd University of Petroleum and Minerals Department of Information and Computer Science Dhahran Saudi Arabia

ISBN: (纸本)9798331523893

This paper presents a comparative study of key metrics for OCR engines in Bangla language processing. PyTesseract (a Python wrapper for Tesseract OCR) and EasyOCR were benchmarked on a novel dataset, "Bangla-CrossHair,"created for testing OCR engines. This dataset combines samples from "Bangla Text Detection and Recognition,""Bangla Handwritten Characters,"and "Bangla Handwritten Words"datasets and includes diverse image types, such as blurred, clear, torn, and tilted. Results show EasyOCR outperforms PyTesseract in several scenarios, while PyTesseract consistently demonstrated faster processing. Since many OCR engines provide pre-trained capabilities, traditional metrics like training and validation accuracy are challenging to measure for some models, including PyTesseract. Instead, Character Level Accuracy, Word Level Accuracy, Levenshtein Distance, Character Error Rate, Word Error Rate, Precision, Recall, F1-Score, and Inference time were measured for an unbiased evaluation. © 2025 IEEE.

关键词： Optical character recognition

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