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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Ghazala Alhadi Eljadi Ahmed Lawgali Salwa Elakeili dept. Computer Sciences Faculty of Information Technology University of Benghazi Beghazi Libya dept. Software Engineering Faculty of Information Technology University of Benghazi Beghazi Libya

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Early detection of Acute Lymphoblastic Leukemia (ALL) is vital for providing appropriate treatment, which requires accurate diagnosis to identify the types of the disease. Therefore, this study focuses on evaluating the performance of deep learning models in classifying different types of leukemia using three pre-trained CNN models: MobileNet V2, DenseNet201, and EfficientNet, to extract deep features. These features are then combined into a single set. An information exchange technique was used to select the best features after merging, and SMOTEENN was employed to address feature imbalance. The results showed that combining features extracted by the pre-trained CNN models achieved high accuracy and reliability, reaching 99.66%, enhancing the performance in accurately and quickly classifying patients. This approach not only improves diagnostic accuracy but also contributes to accelerating the decision-making process for treatment, enabling doctors to better tailor therapies and thereby improve patient outcomes. The findings underscore the importance of ongoing research in this field and provide future recommendations for enhancing performance and increasing classification accuracy in similar medical applications.

关键词： Support vector machines Accuracy Merging Leukemia Medical treatment Feature extraction Convolutional neural networks Reliability Blood Medical diagnostic imaging

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A Robust Methodology for Fruit Quality Prediction and Estimation Using Intelligent Learning Based Image Processing Logic

A Robust Methodology for Fruit Quality Prediction and Estima...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Malathi, K. Babu, Vunnava Dinesh Ramkumar, M. Ayyadurai, M. Vivek, K. Aljawawdeh, Hamzeh J. Saveetha University Department of Cse Chennai India Rv Institute of Technology Department of Cse Guntur India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Department of software engineering Faculty of information technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Fruit quality prediction plays a vital role in agricultural and food industries, ensuring that only high-quality produce reaches the consumer. Traditional methods of assessing fruit quality rely on manual inspection, which is both time-consuming and prone to human error. This paper presents a robust methodology for fruit quality prediction and estimation, utilizing a hybrid deep learning model that integrates DenseN et for feature extraction with LightGBM for efficient classification. The proposed model is designed to automate the process of fruit quality assessment by analyzing key attributes such as color, texture, and shape from high-resolution fruit images. The Fruit360 dataset was used for training and testing, with images classified into categories such as fresh, ripe, overripe, and damaged. A comprehensive comparison with nine existing models, including SVM, Random Forest, and XGBoost, was conducted using performance metrics such as accuracy, precision, recall, and Fl-score. The proposed model achieved an accuracy of 96.78%, significantly outperforming all other models. Furthermore, the model's precision and recall reached 95.33% and 95.67%, respectively, demonstrating its reliability in distinguishing high-quality fruits from low-quality ones. © 2024 IEEE.

关键词： Fruits

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Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

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Computers, Materials & Continua 2023年第9期76卷 3763-3781页

作者： Sayyid Kamran Hussain Ali Haider Khan Malek Alrashidi Sajid Iqbal Qazi Mudassar Ilyas Kamran Shah Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering Faculty of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer Science Applied CollegeUniversity of TabukTabukSaudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl Hofuf31982Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl HofufSaudi Arabia

As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their *** imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical *** creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection *** the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of *** study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical *** is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and *** classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation *** testing shows that the method is reliable and efficient.

关键词： Deep learning multi-classification focal loss CNN eye disease

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： Computer vision

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Smart Farmer Education System for Disease Identification, Severity Assessment and Infestation Management (Brinjal) 17

Smart Farmer Education System for Disease Identification, Se...

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17th IEEE International Conference on Industrial and information Systems, ICIIS 2023

作者： Bandara, Keshara Vidhanaarachchi, Samitha Nanayakkara, A.A.E. Kulasinghe, Kavindu Rajapakshe, Sanka Wickramasinghe, Asela Yapa, Kanishka Sri Lanka Institute of Information Technology Department of Information Technology New Kandy Road Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka University of Ruhuna Faculty of Agriculture Department of Soil Science Mapalana Kamburupitiya Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350323634

The agriculture industry is one of the most significant sources of foreign exchange and employment in the Sri Lankan market. Therefore, small crops play a crucial role in ensuring the food security of the population as they are integral components of Sri Lankan cuisine. However, industry experts have identified inefficient disease and pest management as a hindrance to production. This issue requires immediate attention and proper action, which can be effectively addressed through the utilization of ICT technologies. Consequently, this research paper proposes a smart system that aims to assist farmers and agriculture professionals by providing them with farmer education, disease and infestation management tools, as well as progression level calculations. The implementation of this system aims to uplift the agriculture industry in Sri Lanka. The developed 'AI-based mobile application system for Brinjal Diseases,' which is based on analyzing the eating patterns of leafhopper damage, presents an innovative approach to identify the most significant threats to Brinjal, specifically focusing on leafhopper damage. This proposed system effectively informs various stakeholders, including the Area Agricultural Research Officer, Seed Certification and Plant Protection Center (SCPPC), Agricultural Service Center, Office of the Register of Pesticides (ROP), Plant Protection Service (PPS), as well as farmers, regarding the dispersion and prevention of brinjal diseases and pest infections. © 2023 IEEE.

关键词： Crowdsourcing

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Self-Configured Workflow Platform for MapReduce Job Execution in Cloud Environment

Self-Configured Workflow Platform for MapReduce Job Executio...

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2022 Applied Informatics International Conference, AiIC 2022

作者： Saadoon, Muntadher Admodisastro, Novia University of Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia Universiti Putra Malaysia Faculty of Computer Science and Information Technology Selangor Serdang43400 Malaysia

ISBN: (数字)9781728184289

ISBN: (纸本)9781728184289

Hadoop MapReduce becomes the essential framework to store, process and analyse big data in large-scale computing environments. The framework provides numerous opportunities to handle data-intensive applications like web crawling and indexing, data mining, anomaly prediction and machine learning. However, manual configurations of the framework parameters expose to human errors and lead to performance overhead and resource consumption penalties. Existing deployment automation solutions intended to mitigate the limitations by only facilitating the application execution workflow without considering crucial conditions that violates the overall performance after setting up the infrastructure parameters. This paper proposes a self-configured workflow platform with a user-friendly collaborating environment that provides automated setups of the framework paraments to obtain ideal performance compared to the native deployment of Hadoop MapReduce. The self-configured workflow platform has been evaluated in experiments on a real Hadoop cloud-based cluster and the results shows that the execution times are comparable to the native application. © 2022 IEEE.

关键词： MapReduce

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Reliability of Multipath Networks with Optimization of the Location of Inter-Path Communication Nodes

Reliability of Multipath Networks with Optimization of the L...

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2023 International Russian Smart Industry Conference, SmartIndustryCon 2023

作者： Bogatyrev, Vladimir A. Tu Le, Anh Abramova, Evgenia A. Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia University ITMO Faculty of Software Engineering and Computer Technology Saint Peterburg Russia

ISBN: (纸本)9781665464291

The article explores the possibility of improving the reliability of a network with multipath routing. A feature of the proposed study is the analysis of the influence of the placement of switching nodes that switch path segments on the probability of network connectivity with servers of the same type and heterogeneous in functionality. The purpose of the article is to increase the reliability of the network, taking into account the influence of the placement of communication nodes that implement the switching of route segments during reconfiguration, on the connectivity of the network with servers that are homogeneous and heterogeneous in functionality. The research involves the construction of a structural reliability model that takes into account the possibility of failures of both communication nodes and the links between them. The reliability model is built taking into account the availability of request sources with full and non-full access connections to the set of network paths. © 2023 IEEE.

关键词： Reliability

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Identifying and Documenting Best Practices in Digital Transformation 23

Identifying and Documenting Best Practices in Digital Transf...

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Proceedings of the 28th European Conference on Pattern Languages of Programs

作者： Mohammad Yusuf Momand Valentino Vranić Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9798400700408

Digital transformation allows organizations to maintain sustainable development and address ongoing challenges. The current digital transformation and advanced technology mega-trend significantly impact society and organizations, making digital technology crucial for public and private organizations, universities, and daily life. Despite the rising demand, there is often a lack of standard policies governing the use of digital technology. Practical experience and literature reveal key challenges in transitioning from traditional to digital systems, including limited public awareness, mindset, ICT skills, government support, and collaboration between organizations and software engineering societies. Governments must establish digital transformation policies and offer software development guidelines for public organizations to digitalize their systems. Additionally, they should provide or support research projects funded by governmental and nongovernmental organizations to identify infrastructure and regulatory issues within Afghanistan’s digital transformation context. This study documents six organizational patterns observed during the transformation of traditional systems into digital systems, illustrating their sequence and explaining the development of a pattern language from these patterns.

关键词： digital transformation organizational patterns patterns software engineering

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Reliability of a Reconfigurable Network with Segment Switching

Reliability of a Reconfigurable Network with Segment Switchi...

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2023 Wave Electronics and its Application in information and Telecommunication Systems, WECONF 2023

作者： Tu, Le Anh Bogatyrev, V.A. Abramova, E.A. ITMO University Faculty of Software Engineering and Computer Technology Saint Peterburg Russia Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia

ISBN: (纸本)9798350348293

The article proposes models of the structural reliability of a multipath routing network with the possibility of its reconfiguration when switching path segments. The models are focused on optimizing the placement of segment switching nodes, aimed at achieving maximum network performance with the option of connecting request sources to the available communication paths with servers. The study considers the options for connecting each source of requests with one of the paths or with all paths for connecting to servers. The paper shows the existence of an optimal placement of path switching nodes, in which the maximum reliability of the system is achieved. © 2023 IEEE.

关键词： Redundancy

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Efficient Video Retrieval Method Based on Transition Detection and Video Metadata information

Efficient Video Retrieval Method Based on Transition Detecti...

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2023 International Conference on System Science and engineering, ICSSE 2023

作者： Do-Tran, Nhat-Tuong Tran, Vu-Hoang Nguyen, Tuan-Ngoc Nguyen, Thanh-Le HCMC University of Technology and Education Faculty of Vehicle and Energy Engineering Ho Chi Minh City Viet Nam HCMC University of Technology and Education Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam Sai Gon University Faculty of Software Engineering Ho Chi Minh City Viet Nam University of Information Technology Faculty of Information Science and Engineering Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350322941

In this paper, we propose an event retrieval support system that quickly finds videos in a large database based on user-entered content. The system addresses the challenges of providing fast and relevant results for a dataset of over 400 hours of videos and developing user-friendly tools. To achieve fast retrieval, we convert the videos into compact semantic features. This involves two steps: (1) Identifying keyframes that represent different content and (2) Extracting semantic features from these frames. We first use the TransNet model to find transition frames, which split the video into scenes with different content. Then we will extract the keyframes which are evenly distributed in these scenes. Finally, the CLIP model is used to extract features from these keyframes and connect them with text. This forms a compact and semantic feature database. When users search with text, we convert it into features and measure similarity with the database using cosine distance, then the most similar video is retrieved. In cases where CLIP model fails, we recommend leveraging news headlines and audio by applying Optical Character Recognition (OCR) and Automatic Speech Recognition (ASR) on videos to form a text database and comparing the entered text with this text database. Experimental results on a Vietnamese media news dataset demonstrate the effectiveness and accuracy of our method. © 2023 IEEE.

关键词： Optical character recognition

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