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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Prince, Nayem Uddin Al Mamun, Md. Abdullah Shagar, Md. Tanvir Miah Emon, Md. Rezaul Karim Sajib, Md. Sahadat Hossen Varendra University Department of Computer Science and Engineering Rajshahi Bangladesh Daffodil International University Department of Software Engineering Dhaka Bangladesh Washington University of Science and Technology United States

ISBN: (纸本)9798350354348

Northern Bangladesh is home to the most lychee growing, which has a major economic impact. Lychee output and quality are reduced by various leaf and fruit diseases. Deep learning is used to construct a disease detection system for lychee diseases to detect them early and accurately. We correctly identified 6,000 photos of healthy and unhealthy lychee foliage and fruits. Six deep learning algorithms - VGG16, CustomCNN, MobileNet, InceptionV3, ResNet50, and Vision Transformer - classified the photos. Normalization and augmentation improved model resilience during data preparation. F1-score, recall, accuracy, and precision were utilized to train and evaluate the models. Vision Transformer (ViT) scores 99.91% in accuracy, recall, and F1-score, outperforming the other models. This achievement shows that ViT can better recognize complex lychee disease traits. The ViT algorithm for disease detection can help Bangladeshi farmers identify illnesses quickly and accurately. Reduced lychee loss and improved fruit quality and attractiveness should boost lychee sales in domestic and international markets. © 2024 IEEE.

关键词： Fruits

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Develop an Ontology for E-Commerce based on a Web Application to Assist Color-blind people 2

Develop an Ontology for E-Commerce based on a Web Applicatio...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Farooq, Muhammad Usman Qamar, Usman Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665498197

Color-blindness is a genetic eye disease. The person who is suffering from this disease cannot see the correct color of life. The human eye consists of rods and cones. Rods are responsible for black and white vision while cones are responsible for color vision. Colorblindness is arisen due to a deficiency of few cones of the eye. E-Commerce application plays an important role in the current era. But most known applications do not handle colorblind people. In the existing research, there is no such solution suggested however in this research an Ontology is proposed for E-commerce applications that assist color-blind people. The proposed ontology provides an understanding of the domain of color-blind disease and the E-commerce domain. The proposed ontology can be visualized by using the OWLViz tool. Validation of this ontology is provided by using a case study. This ontology can be further expanded by adding more concepts to it. This ontology can also be used by any GUI-based application by applying minor changes in concepts. © 2022 IEEE.

关键词： Color

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Evolutionary Optimization with a Simplified Helper Task for High-Dimensional Expensive Multiobjective Problems

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-32页

作者： Wu, Xunfeng Lin, Qiuzhen Zhou, Junwei Liu, Songbai Coello Coello, Carlos A. Leung, Victor C. M. College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China Mexico City07300 Mexico Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

In recent years, surrogate-Assisted evolutionary algorithms (SAEAs) have been sufficiently studied for tackling computationally expensive multiobjective optimization problems (EMOPs), as they can quickly estimate the qualities of solutions by using surrogate models to substitute for expensive evaluations. However, most existing SAEAs only show promising performance for solving EMOPs with no more than 10 dimensions, and become less efficient for tackling EMOPs with higher dimensionality. Thus, this article proposes a new SAEA with a simplified helper task for tackling high-dimensional EMOPs. In each generation, one simplified task will be generated artificially by using random dimension reduction on the target task (i.e., the target EMOPs). Then, two surrogate models are trained for the helper task and the target task, respectively. Based on the trained surrogate models, evolutionary multitasking optimization is run to solve these two tasks so that the experiences of solving the helper task can be transferred to speed up the convergence of tackling the target task. Moreover, an effective model management strategy is designed to select new promising samples for training the surrogate models. When compared to five competitive SAEAs on four well-known benchmark suites, the experiments validate the advantages of the proposed algorithm on most test cases. © 2024 held by the owner/author(s).

关键词： Multitasking

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Task Scheduling in Fog Computing: Parameters, Simulators and Open Challenges 6

Task Scheduling in Fog Computing: Parameters, Simulators and...

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6th Global Conference on Wireless and Optical Technologies, GCWOT 2023

作者： Saad, Muhammad Qureshi, Rehan Inam Rehman, Aqeel Ur Sir Syed University of Engineering and Technology Software Engineering Department Karachi Pakistan UIT University Computer Science Department Karachi Pakistan

ISBN: (纸本)9798350333718

As Internet of Things (IoT) is evolving very rapidly which is producing massive amount of data and normally all this data is shifted towards the cloud for further execution which puts burden on the cloud and doesn't fulfill QoS requirements. In order to overcome this problem computing facility were extended towards the edge of the network and the concept is known as Fog computing. Due to heterogeneity of fog nodes and dynamic user requirements, task scheduling in fog computing has become the big challenge. This paper covers the literature review which classify the task scheduling algorithm in terms of approaches, it highlights the parameters mostly used, it also covers the analysis among the available simulation tools and highlight the open issues and challenges which will help the researchers for their problem formulation. © 2023 IEEE.

关键词： Fog computing

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Enhancing ASD Diagnosis with Contrastive and Non-Contrastive Models from Neuroimaging Data 4

Enhancing ASD Diagnosis with Contrastive and Non-Contrastive...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Jha, Abhishek Mehta, Ishita Khan, Kainat Katarya, Rahul Delhi Technological University Department of Mechanical Engineering New Delhi India Delhi Technological University Department of Software Engineering New Delhi India Delhi Technological University Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350352931

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition which impacts social interaction, communication, and behavior. This study introduces a self-supervised learning approach for ASD prediction using resting-state functional MRI (rs-fMRI) scans from the ABIDE I dataset, employing contrastive and non-contrastive models. The contrastive model enhances learning by maximizing agreement between augmented views of the same scan, while the non-contrastive model reconstructs input data through an autoencoder. Both models, pre-trained on ImageNet, were fine-tuned for ASD classification using Resnet18. Our results show that the contrastive model achieved 97.6% accuracy, with a precision of 95.7%, recall of 99.2%, and F1 score of 97.4%. The non-contrastive model reached 95.4% accuracy, with precision, recall, and F1 scores of 99.3%, 96.46%, and 97.82%. These findings highlight the performance of self-supervised learning in ASD diagnosis, showcasing robust pattern-capturing capabilities for neuroimaging data. © 2024 IEEE.

关键词： Contrastive Learning

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Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

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Operations Research Forum 2024年第3期5卷 58页

作者： Wei, Li Solihin, Mahmud Iwan Saruchi, Sarah ‘Atifah Astuti, Winda Hong, Lim Wei Kit, Ang Chun School of Computer Science and Technology (School of Software) Guangxi University of Science and Technology Guangxi 545006 China Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur 56000 Malaysia Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia Computer Engineering Department Automotive and Robotics Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Anomaly detection computer vision Deep learning Defect detection High-precision cylindrical parts Image processing Optical illumination

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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Intelligent Agents in Educational Institutions: NEdBOT - NLP-based Chatbot for Administrative Support Using DialogFlow

Intelligent Agents in Educational Institutions: NEdBOT - NLP...

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2022 IEEE International Conference on Agents, ICA 2022

作者： Ali, Muhammad Shahroze Azam, Farooque Safdar, Aon Anwar, Muhammad Waseem Department of Computer & Software Engineering Islamabad Pakistan

ISBN: (数字)9781665469364

ISBN: (纸本)9781665469364

Artificial intelligence (AI)-based chatbot systems have seen increased adaption in the educational domain in recent years owing to increased sophistication in the AI domain. However, most of the communication between students and educational institutions is still performed physically and causes major administrative overhead, especially during the time of admission. Contemporary pattern-matching-based and generative-based chatbots underperform to queries outside a limited scope, grammatically and structurally ambiguous inputs, outliers to pre-defined rule-set, and longer response times for a huge knowledge base. We proposed a NEdBOT-An NLP-based Educational Bot, developed by Natural Language Processing models integrated within the DialogFlow platform utilizing a Retrieval-based approach. We evaluate the developed chatbot on a custom dataset generated for the admissions use case of a prominent university. We used an objective evaluation criterion with real-world users to achieve an intent classification accuracy of 76.8% at an average mean response time of 216.43ms per query and a user-friendliness score of 72% on the System Usability Scale (SUS). The results demonstrate the proposed approach's ability to create robust, reliable, responsive, and user-friendly web-based smart chatbots that are highly scalable with the capability to handle wider scopes and vague inputs with ease. © 2022 IEEE.

关键词： Natural language processing systems

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Recently emerging trends in big data analytic methods for modeling and combating climate change effects

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Energy Informatics 2024年第1期7卷 6页

作者： Ikegwu, Anayo Chukwu Nweke, Henry Friday Mkpojiogu, Emmanuel Anikwe, Chioma Virginia Igwe, Sylvester Agbo Alo, Uzoma Rita Software Engineering Department Veritas University Abuja Nigeria Computer Science Department David Umahi Federal University of Health Sciences Ebonyi State Uburu Nigeria Computer Science Department Alex Ekwueme Federal University Ebonyi Ndufu-Alike Nigeria Computer Science Department Ebonyi State University Ebonyi State Abakaliki Nigeria

Big climate change data have become a pressing issue that organizations face with methods to analyze data generated from various data types. Moreover, storage, processing, and analysis of data generated from climate change activities are becoming very massive, and are challenging for the current algorithms to handle. Therefore, big data analytics methods are designed for significantly large amounts of data required to enhance seasonal change monitoring and understand and ascertain the health risks of climate change. In addition, analysis of climate change data would improve the allocation, and utilisation of natural resources. This paper provides an extensive discussion of big data analytic methods for climate data analysis and investigates how climate change and sustainability issues can be analyzed through these approaches. We further present the big data analytic methods, strengths, and weaknesses, and the essence of analyzing big climate change using these methods. The common datasets, implementation frameworks for climate change modeling, and future research directions were also presented to enhance the clarity of these compelling climate change analysis challenges. This big data analytics method is well-timed to solve the inherent issues of data analysis and easy realization of sustainable development goals. © The Author(s) 2024.

关键词： Data Analytics

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The Impact of Blockchain Technology for Evaluation of Electronic Medical Certification

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SN computer Science 2025年第5期6卷 1-14页

作者： Azam Zia, M. Anwar, Munazza Mehmood, Faisal Razzaq, Abdul Asif, Muhammad Department of Computer Science University of Agriculture Faisalabad Punjab Faisalabad 38000 Pakistan Department of Civil and Transportation Engineering Shenzhen University Shenzhen 518060 China Computer Science Muhammad Nawaz Shareef University of Agriculture Punjab Multan 59300 Pakistan Department of Computer and Software Engineering Information Technology University (ITU) Punjab Lahore Pakistan

Evaluation of Electronic Medical Certification (EMC) is integral to today's healthcare systems since they are a consolidated database of individual patients' medical histories. Security measures and privacy issues are standard in electronic medical certification. Incompatible systems make sharing data across healthcare providers difficult, which may lead to data breaches and a lack of patient authority over their medical data. Blockchain has become essential for real-world corporate applications where confidentiality of information is paramount. The handling of security in medical records through a decentralized peer-to-peer network might be revolutionized by blockchain technology and improving the protection of the interchange of healthcare records. This study proposes a lightweight flask framework-based blockchain strategy to store and display patient status and transaction log information about their disease diagnosis. In a potential Blockchain system, the diseased patients act as assets, and their records contain any prior illnesses as blocks. The report analyzes a lot of national and international EHR standards. It identifies which of these interoperability needs to be addressed, discusses the interoperability challenges in but also outlines the standards. Current blockchain-based EHR frameworks. Without the suggested has the requirement for centralized systems. By using this framework, the healthcare industry can be provided safer ways to while also providing the exchange of health information. It offers features of immutability, security, and user control over stored records. This work contributes to an understanding of what potential Blockchain technology has offered an interoperable application in the EHR frameworks. Meet the blockchain-based EHR framework. Multiple national and international EHR standards. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Cryptography Decentralized Electronic medical certification (EMC) Transaction

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