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5th International Conference on Advancements in Computing, ICAC 2023

作者： Ranasinghe, Chathra Navoda Ranasinghe, Kavindu Ushan Munasighe, Niketh Priyaratne, Manul Harshanath, Buddika Rajapakse, Samantha Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

This research addresses a critical gap in Sri Lanka's tourism infrastructure: the absence of a comprehensive smart location-based recommendation system with a route planner and a multilingual AI chatbot. To date, no integrated solution has been developed to cater to the diverse preferences of tourists, offer community-validated recommendations, and bridge language barriers. This research is pioneering the creation of these novel systems, with a clear focus on enhancing the tourism experience and supporting the country's economy. The smart recommendation system harmonizes various recommendation techniques, encompassing user preferences, community-approved choices, and hidden gems. By integrating these aspects, the system aims to provide tourists with personalized, community-endorsed recommendations while introducing them to unique destinations, thus addressing the existing void in Sri Lanka's tourism landscape. Furthermore, the AI-based multilingual chatbot tackles the language barrier head-on, enabling tourists to communicate effortlessly in their native language, a feature hitherto unavailable in the Sri Lankan tourism sector. This innovation aims to break down language barriers, making the country more accessible to a global audience. Through advanced methodologies and cutting-edge technology, this research strives to fill these critical gaps, promising a brighter future for Sri Lanka's tourism and its economic prosperity. © 2023 IEEE.

关键词： Tourism

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Chest Radiographs Based Pneumothorax Detection Using Federated Learning

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computer Systems science & engineering 2023年第11期47卷 1775-1791页

作者： Ahmad Almadhor Arfat Ahmad Khan Chitapong Wechtaisong Iqra Yousaf Natalia Kryvinska Usman Tariq Haithem Ben Chikha Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Science and Software Engineering International Islamic UniversityIslamabadPakistan Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Prince Sattam bin Abdulaziz University AlKharjSaudi Arabia

Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse,causing air to enter the pleural cavity,the area close to the lungs and chest *** most persistent disease,as well as one that necessitates particular patient care and the privacy of their health *** radiologists find it challenging to diagnose pneumothorax due to the variations in *** learning-based techniques are commonly employed to solve image categorization and segmentation ***,it is challenging to employ it in the medical field due to privacy issues and a lack of *** address this issue,a federated learning framework based on an Xception neural network model is proposed in this *** pneumothorax medical image dataset is obtained from the Kaggle *** preprocessing is performed on the used dataset to convert unstructured data into structured information to improve the model’s ***-max normalization technique is used to normalize the data,and the features are extracted from chest Xray *** dataset converts into two windows to make two clients for local model *** neural network model is trained on the dataset individually and aggregates model updates from two clients on the server *** decrease the over-fitting effect,every client analyses the results three *** 1 performed better in round 2 with a 79.0%accuracy,and client 2 performed better in round 2 with a 77.0%*** experimental result shows the effectiveness of the federated learning-based technique on a deep neural network,reaching a 79.28%accuracy while also providing privacy to the patient’s data.

关键词： Privacy preserving pneumothorax disease federated learning chest x-ray images healthcare machine learning deep learning

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Multi-branch Person Re-identification Net 2

Multi-branch Person Re-identification Net

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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Chen, Zhihao Ge, Yiyuan Zhang, Ji Gao, Xiang Beijing Information Science and Technology University Computer School Beijing China Jilin University Department of Software Engineering Changchun China Beijing Information Science and Technology University School of Instrument Science and Opto-Electronics Engineering Beijing China Xihua University School of Mechanical Science and Engineering Chengdu China

ISBN: (纸本)9798350331417

Person ReID is a complex and important problem within the domain of computer vision. Its objective is to identify and match individuals in video sequences, often captured from different camera viewpoints. By comparing visual characteristics, poses, and clothing of pedestrians, Person ReID enables the tracking and recognition of the same person across various scenarios. This paper introduces a Multi-Branches Person Re-identification Net (MPRN), a novel framework for person re-identification. MPRN leverages OSNet as the backbone and establishes a multi-branch structure on top of it, effectively integrating both local and global features. Experimental results demonstrate that the combination of OSNet and the multi-branch structure yields highly competitive results. Specifically, it achieves a mean average precision (mAP) of 90.5% and a rank-1 accuracy of 96% on the Market-1501 dataset, as well as an mAP of 85.5% and a rank-1 accuracy of 87.7% on the CHUK03-L dataset. © 2023 IEEE.

关键词： Feature Extraction Multi-Branch Structure Person Re-identification

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Development of a Prediction System for Criminality and Suicidal in Sri Lanka 5

Development of a Prediction System for Criminality and Suici...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Ranasinghe, D.P.D. Fernando, R.D.T.S. Girambe, D.G. Arambegama, D.M.H.K.D. Chathurika, Bhagyanie Jayasinghe, Dulani Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

This research confronts the growing challenges of rising suicide rates and crime in Sri Lanka through a dual approach, harnessing advanced machine learning and time series analysis. The innovative methodology, merging 'Feature Importance Weighting and Ensemble of Diverse Trees with the Random Forest Regressor,' achieves impressive accuracy of 93% for factors and 94% for methods. Additionally, the accuracy rates of 94% for education and 93% for occupationbased predictions enhance this comprehensive approach, offering a profound understanding of suicidal incidents and enabling precise prevention strategies. Simultaneously this study employs an ensemble of AutoRegressive Integrated Moving Average (ARIMA) models for crime prediction. This approach consistently attains high accuracy, often surpassing 90%, such as 96% and 97%. Utilizing multiple models enhances predictive accuracy and hotspot identification, granting law enforcement and policymakers flexibility to address enduring yearly hotspots and evolving dynamic hotspots. This research bridges the comprehension of suicide and crime dynamics in Sri Lanka, providing invaluable insights for evidence-based policies and targeted interventions. © 2023 IEEE.

关键词： Forecasting

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Facial Skincare Product Suggestion with Product Popularity and Post Recommendation Care 4

Facial Skincare Product Suggestion with Product Popularity a...

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4th International Informatics and software engineering Conference, IISEC 2023

作者： Samarakoon, H.M.W.T.C.B. Ovitigala, A.U. Wijesinghe, G.A.T.T. Weerasinghe, K.M.L.P. Karunathilake, G.K.B.V. Weerathunga, Ishara Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350318036

Skin health significantly affects a person's overall wellbeing, which includes both physical and emotional aspects. The objective of this study is to develop an integrated system for recommending skincare products that integrates image processing, machine learning, sentiment analysis, and user feedback analysis to deliver individualized skincare advice and boost user satisfaction. Utilizing deep learning algorithms and face cues, the system uses user image analysis to detect important skin features, skin type, acne severity, and dark spots with extraordinary accuracy. Using sentiment analysis and clustering, the program identifies popular skincare products and tailors' recommendations to user preferences. Recommendations are further personalized by collaborative filtering. By using image analysis to identify allergies and other negative effects, user safety is increased. The technique prioritizes data safety and includes model training, sentiment analysis, image processing, and user input analysis. Initial findings demonstrate the excellent accuracy of skin issue detection and severity prediction. By allowing users from a variety of user demographics to make informed skincare decisions, this complete framework has the potential to change the skincare industry. © 2023 IEEE.

关键词： Sentiment analysis

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An Approach for Real Time computer User Action Monitoring 5

An Approach for Real Time Computer User Action Monitoring

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Alahakoon, Dulshan Samarakoon, Tharindu Wickramanayake, Indika Sakalasooriya, Kushani Kasthurirathna, Dharshana Karunasena, Anuradha Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

computers play a significant part in the modern world. The rapid increase in computer use, especially after the COVID-19 pandemic, raised concerns about the potential negative impact on user health. Sitting posture, eye health, stress, and sleep are the most important factors when considering the health of daily computer users. It is more important to prevent issues from occurring than to find solutions once they have occurred. To address this issue, a Real-Time computer User Action Monitoring Approach is proposed that uses machine learning techniques to monitor and preserve user health effectively. Importantly, this solution leverages only the built-in webcam, eliminating the need for costly external equipment and ensuring affordability. The proposed solution consists of four key components: sitting posture detection and correction, eye tracking for eye health and comfort, stress detection and reduction techniques, and drowsiness detection for enhanced productivity and safety. This approach assists users in avoiding harmful behaviors, fosters healthful computer use by providing real-time feedback, and offers personalized recommendations. © 2023 IEEE.

关键词： Image processing

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Deep Learning-Based Probability Model for Traffic Information Estimation

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第3期7卷 592-607页

作者： Sun, Zhaoshan Pan, Jeng-Shyang Pan, Tien-Szu Chen, Chi-Hua College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Collage of Computer Science and Engineering Shandong University of Science and Technology China Chaoyang University of Technology Taiwan Fujian University of Technology China Department of Electronic Engineering National Kaohsiung University of Science and Technology 415 Chien-Kung Road Kaohsiung807 Taiwan College of Computer and Data Science College of Software Fuzhou University Fuzhou350116 China

This study proposes a novel traffic information estimation method based on deep learning and cellular floating vehicle data (CFVD). In this paper, a probabilistic analysis model based on deep learning is proposed to consider the relationship between vehicle speed and communication behaviors. Then, a vehicle speed estimation method based on the proposed probabilistic analytical model is proposed to estimate vehicle speed. For traffic flow estimation, normal location update is adopted to estimate traffic flow. The estimated vehicle speed and the estimated traffic flow can be gathered to estimate traffic density. The proposed method is verified by the simulation tool and the experiment results revealed that the accuracies of estimated vehicle speed, estimated traffic flow, and estimated traffic density are 96.36%, 99.80%, and 96.45%, respectively. Thus, this method estimates traffic information accurately and helps improve the performance of the intelligent transportation system (ITS). © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Vehicles

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A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1476-1489页

作者： Shoufei Han Kun Zhu MengChu Zhou Xiaojing Liu Haoyue Liu Yusuf Al-Turki Abdullah Abusorrah College of Computer Science and Technology Nanjing University of Aeronautics and Astronauticsand also with the Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjing 211106China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand also with the K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing *** goal is to find a Pareto front and as many equivalent Pareto optimal solutions as *** some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions *** this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision *** first extend a latest single-objective fireworks algorithm to handle *** we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special ***,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization *** results show that the proposed algorithm is superior to compared algorithms in solving ***,its runtime is less than its peers'.

关键词： Adaptive strategy fireworks algorithm multimodal multiobjective optimization problems(MMOP)

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GAN and IEC Approach for Image Generation 6

GAN and IEC Approach for Image Generation

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6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022

作者： Shukla, Amar Mahdi, Hussain Falih Tandon, Ishita Singh, Rajkamal Choudhury, Tanupriya School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India University of Diyala Department of Computer and Software Engineering Baquba Iraq

ISBN: (数字)9781665470131

ISBN: (纸本)9781665470131

Generative adversarial networks (GANs) are capable of learning deep representations efficiently without requiring a significant amount of annotation. Through Backpropagation, signals are derived from one network by another through a competitive process. This article presents a machine learning framework that incorporates generative adversarial networks (GANs) and interactive evolutionary computation (IEC) to generate images and to resample those images for the detection of fake images. In our study, we found that GANs trained on a particular domain could produce reliable and compact phenotypic maps. Through the use of a user research method, participants able to produce images closely matched target images, demonstrating the advantages of this unique approach. © 2022 IEEE.

关键词： Generative adversarial networks

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Identification of Diabetic Related Eye Diseases Using Deep Learning 5

Identification of Diabetic Related Eye Diseases Using Deep L...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Wijesinghe, K.H. Dilshan, U.K.T. Dilshan, K.B.G.L. Tharupathi, M.A.U. Kasthuriarachchi, Sanvitha Rajapaksha, Samantha Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

Visual disability seems to be increasing and widespread worldwide. Current diagnostics require manual expertise for diagnosis. The advancement of Artificial Intelligence research for medical applications has been in focus in recent years. Fundus images and oct images are valuable sources of information to help ophthalmologists diagnose vision disorders or eye diseases. Early detection can improve the chances of recovery and prevent blindness. The application of an intelligent computer-based approach to classifying different eye disorders is extremely beneficial for both diagnosis and prevention. The proposed expert system for diagnosing diabetic macular edema, diabetic retinopathy, cataracts, and glaucoma diseases is discussed in this research study. It is based on deep learning image processing techniques. For the detection of eye diseases, software used retinal and OTC images. This study presents Yolo, UNET, customized CNN, and Auto ML model-based system for detecting eye diseases. For the tasks of disease identification and grading, the suggested methods each obtained more than 87% accuracy. The primary objective of this study is to achieve automated identification of eye disease images. This is accomplished through the implementation of a deep learning model alongside advanced image processing techniques, enabling the automatic classification of eyes into either a healthy or diseased category. © 2023 IEEE.

关键词： Diagnosis

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