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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Gamage, Madhuransi Agrahere Arachchi, Ranruwini Matara Naotunna, Sawandi Rubasinghe, Tharushi Silva, Chamari Siriwardana, Samanthi 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)9781665408622

Academic Depression is a widespread problem among undergraduate students in Sri Lanka. It is exhausting and has a detrimental impact on students' academic life. Therefore, the development of a technique to estimate the probability of depression among undergraduates is a blessed respite. Depression is mostly caused by a failure to check students' psychological well-being on a regular basis. Identifying depression at the college level, leading the students to get proper therapy treatments. If a counselor detects depression in a student early enough, he/she can successfully assist the student in overcoming depression. However, keeping track of the substantial changes that occur in students because of depression becomes challenging for the counselor with a considerable number of undergraduates. The advancement of image processing and machine learning fields has contributed to the creation of effective algorithms capable of identifying depression probability. Depression Possibility Detection Tool (DPDT) is considered an effective automated tool that brings the depression probability of a certain student. In DPDT, the result is generated by concerning four main strategies. They are facial expressions, eye movements, behavior changes (step count and phone usage), and physical conditions (heart rate and sleep rate). Convolutional Neural Network (CNN) with Visual Geometry Group 16 (VGG16) model, Residual Neural Network (ResNet-50), Random Forest (RF) classifier is the main models and techniques used in the system. More than 93% of accuracy was generated in every trained model. The paper concludes the system overview along with four strategies, literature review, methodologies, conclusion, and future works. © 2021 IEEE.

关键词： Students

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Automated Sinhala News Platform Based on Machine Learning and Deep Learning 3

Automated Sinhala News Platform Based on Machine Learning an...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Ashani Malsha, N.P.G. Dulmini Heshani, K. Ransara, R. Kavishka Ayesh Bandara, D.M.D.D. Suriyaa Kumari, P.K. Anuththara Kuruppu, Thilmi Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Over the years, newspapers are taken as a major activity in citizens. Due to digitalization, online newspapers get famed, and eventually, it seems to be an ideal adaptation for their busy lifestyle. While there are several Sinhala News platforms, nonetheless, provide an advance and better user experience similar to English news platforms like google news. To fill this gap the proposed news platform, dispense personalized news recommendation based on user behaviors and user emotions. To replace the manual news categorization, the proposed system is armed with automatic news categorization. An automatically unnecessary comment removing feature was also added to the proposed system as an extra feature. The system implementation is based on deep learning, machine learning, NLP, sentimental analysis, and image processing techniques where it can provide a better user experience and a new experience. © 2021 IEEE.

关键词： Deep learning

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Transparency Paths - Documenting the Diversity of User Perceptions 21

Transparency Paths - Documenting the Diversity of User Perce...

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29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021

作者： Giunchiglia, Fausto Kleanthous, Styliani Otterbacher, Jahna Draws, Tim Department of Information Engineering and Computer Science University of Trento Italy Open University of Cyprus and Cyens Centre of Excellence Cyprus Web Information System and Department of Software Technology Tu Delft Netherlands

ISBN: (纸本)9781450383677

We are living in an era of global digital platforms, eco-systems of algorithmic processes that serve users worldwide. However, the increasing exposure to diversity online - of information and users - has led to important considerations of bias. A given platform, such as the Google search engine, may demonstrate behaviors that deviate from what users expect, or what they consider fair, relative to their own context and experiences. In this exploratory work, we put forward the notion of transparency paths, a process by which we document our position, choices, and perceptions when developing and/or using algorithmic platforms. We conducted a self-reflection exercise with seven researchers, who collected and analyzed two sets of images;one depicting an everyday activity, "washing hands,"and a second depicting the concept of "home."Participants had to document their process and choices, and in the end, compare their work to others. Finally, participants were asked to reflect on the definitions of bias and diversity. The exercise revealed the range of perspectives and approaches taken, underscoring the need for future work that will refine the transparency paths methodology. © 2021 ACM.

关键词： Transparency

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A Hybrid Structural Building Data Mining System Based on the Data Tracking through Devices 1

A Hybrid Structural Building Data Mining System Based on the...

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1st International Conference on Advances in Computational science and engineering, ICACSE 2020

作者： Sangeerani Devi, A. Ravi Prasad, B. Loganathan, Venkateshwaran Saurav, Swapnil Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology Telangana Dundigal India Department of Information Technology KGiSL Institute of Technology Tamil Nadu Coimbatore India Department of Problem Management Service Now Software India Pvt. Ltd Telangana Hyderabad India

One of several traditional megaprojects is underground construction, given its long building time high building expense and possible risks. In tunnel engineering, trench boring devices are generally used to increase work performance and safety. During the tunnelling process, system has recorded vast volumes of tracking data to ensure building safety. The processing of vast real-time surveillance data also lacks successful techniques, and, in many situations, it must be performed manually that pose possible safety hazards. This paper suggests an approach for hybrid data mining (DM) to automatically process the TBM data for real-time tracking. Three separate DM strategies are merged in order to improve the operation of mining also to help security management. The sequential pattern method is executed to remove connections between TBM parameters in order to give people the expertise needed for an irregular on-site judging. A random forest model is built to identify training data in order to complement knowledge needed for building decision-making system. Finally, neural network models measure the penetration rate (ROP) in order to detect irregular data and to alert early. In the case of a tunnel project in China, the suggested technique was applied, and the findings of the application concluded that the approach offered a reliable and effective way of evaluating TBM protection management data in real time during buildings. © Published under licence by IOP Publishing Ltd.

关键词： Data mining

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Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Aymene Berkani, Mohamed Rafik Chouchane, Ammar Himeur, Yassine Abdennebi, Anes Sagiroglu, Seref Amira, Abbes Medea Algeria University Biskra University Center of Barika Laboratory of LI3C Amdoukal Road Barika05001 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates École de Technologie Supérieure Software and Information Technology Engineering Montréal Canada Analytics Security R&D Center Gazi University Artificial Intelligence and Big Data Ankara Turkey University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331527396

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments. © 2024 IEEE.

关键词： Federated learning

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RDH-DES: Reversible Data Hiding over Distributed Encrypted-Image Servers Based on Secret Sharing

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ACM Transactions on Multimedia Computing, Communications and Applications 2023年第1期19卷 5-5页

作者： Xiong, Lizhi Han, Xiao Yang, Ching-Nung Xia, Zhihua Engineering Research Center of Digital Forensics Ministry of Education School of Computer and Software Nanjing University of Information Science and Technology No. 219 Ningliu Road Nanjing Jiangsu210044 China Department of Computer Science and Information Engineering National Dong Hwa University No. 1 Sec. 2 Da Hsueh Rd. Hualien974301 Taiwan

Reversible Data Hiding in Encrypted Image (RDHEI) schemes may redistribute the data hiding procedure to other parties and can preserve privacy of the cover image. Recently, cloud computing technology has led to the rapid growth of networked media, and many multimedia rights are owned by multiple parties, such as a film's producer and multiple distributors. Thus, the data hiding task could be distributed to multiple distributed servers. Multi-party data hiding has become an important demand for networked media. In addition, it is essential to preserve multi-server and multi-message privacy and data integrity. However, most of the RDHEI schemes involve only one data hider. That inspired us to design the secure multi-party embedding over distributed encrypted-image servers as a solution for multi-party RDHEI applications. In this article, we propose a novel Reversible Data Hiding over Distributed Encrypted-Image Servers (RDH-DES) based on secret sharing. The Chinese remainder theorem, secret sharing, and block-level scrambling are developed as a lightweight cryptography to generate the encrypted image shares. These shares are distributed to different image servers and are used to embed secret data in the proposed framework. The marked encrypted image can be constructed through the marked encrypted shares from different parties, and the decryption and extraction can be completed by the receiver. The experimental results and theoretical analysis have demonstrated that the proposed scheme is secure and effective. © 2023 Association for Computing Machinery.

关键词： Steganography

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StackTHP: A stacking ensemble model for accurate prediction of tumor-homing peptides in cancer therapy

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computers in Biology and Medicine 2025年 189卷 109958-109958页

作者： Raihan, Fazla Rabby Abdulrazak, Lway Faisal Rahman, Md. Ashikur Ali, Md Mamun Ibrahim, Sobhy M. Ahmed, Kawsar Bui, Francis M. Mahmud, Imran Department of Software Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh Electrical Engineering Technical College Middle Technical University Baghdad Iraq Department of Computer Science Cihan University Sulaimaniya Kurdistan Region Sulaimaniya46001 Iraq Division of Biomedical Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Department of Biochemistry College of Science King Saud University P.O. Box: 2455 Riyadh11451 Saudi Arabia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh

The tumor-homing peptides (THPs) have emerged as one of the attractive resources for targeted cancer therapy, being able to bind and penetrate tumor cells selectively while ignoring adjacent healthy tissues. Therefore, the computational models to predict THPs became popular very rapidly, since laboratory methods are slow and resourceful. Herein, we are proposing StackTHP, a newly developed stacking-ensemble model aimed at further improving THP prediction accuracy. StackTHP implements multiple feature extraction methods, including amino acid composition (AAC), and pseudo amino acid composition (PAAC) together with classical machine learning classifiers like Extra Trees, Random Forest, and AdaBoost, while the logistic regression-based meta-classifier is used for the stacking framework. StackTHP outperformed all other models, producing an accuracy of 91.92 %, Matthew's correlation coefficient (MCC) of 0.8415, AUC of 0.977 on benchmark datasets, indicates that it is better than approaches attempted earlier and provides a robust solution for proceeding towards the discovery and development of peptide-based cancer therapies. Future research will focus on the application of StackTHP over more diverse sets of data along with some hybrid methods to enhance the prediction capability. The dataset and the code are available at the following link: https://***/Ashikur562/StackTHP. © 2025 The Authors

关键词： Prediction models

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License Plate Detection and Recognition by Convolutional Neural Networks 1

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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Soghadi, Zahra Taleb Suen, Ching Y. Department of Computer Science and Software Engineering Concordia University MontrealQC Canada

ISBN: (数字)9783030598303

ISBN: (纸本)9783030598297

The current advancements in machine intelligence have expedited the process of recognizing vehicles and other objects on the roads. Several methods including Deep Learning techniques have been proposed recently for LPR, yet those methods are limited to specific regions or privately collected datasets. In this paper, we propose an end-to-end Deep Convolutional Neural Network system for license plate recognition that is not limited to a specific region or country. We apply a modified version of YOLO v2 to first recognize the vehicle and then locate the license plate. Moreover, through the convolutional procedures, we improve an Optical Character Recognition network (OCR-Net) to recognize the license plate numbers and letters. Our method performs well for different vehicle types. Our system overcomes tilted and distorted license plate images and performs adequately under various illumination conditions, and noisy backgrounds. Our experimental results on 4,837 images of stationary and moving vehicles (cars, buses, motorbikes, and trucks) from different countries show that our proposed system achieved recognition rates between 88.5% and 98.04%, outperforming the state-of-the-art commercial and academic methods for challenging images. © 2020, Springer Nature Switzerland AG.

关键词： Optical character recognition

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Edge computing service deployment and task offloading based on multi-task high-dimensional multi-objective optimization

arXiv

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arXiv 2023年

作者： Guo, Yanheng Zhang, Yan Wu, Linjie Li, Mengxia Cai, Xingjuan Chen, Jinjun Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Shanxi Taiyuan030024 China State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia

The Mobile Edge Computing (MEC) system located close to the client allows mobile smart devices to offload their computations onto edge servers, enabling them to benefit from low-latency computing services. Both cloud service providers and users seek more comprehensive solutions, necessitating judicious decisions in service deployment and task offloading while balancing multiple objectives. This study investigates service deployment and task offloading challenges in a multi-user environment, framing them as a multi-task high-dimensional multi-objective optimization (MT-HD-MOO) problem within an edge environment. To ensure stable service provisioning, beyond considering latency, energy consumption, and cost as deployment objectives, network reliability is also incorporated. Furthermore, to promote equitable usage of edge servers, load balancing is introduced as a fourth task offloading objective, in addition to latency, energy consumption, and cost. Additionally, this paper designs a MT-HD-MOO algorithm based on a multi-selection strategy to address this model and its solution. By employing diverse selection strategies, an environment selection strategy pool is established to enhance population diversity within the high-dimensional objective space. Ultimately, the algorithm’s effectiveness is verified through simulation experiments. © 2023, CC BY-NC-ND.

关键词： Multiobjective optimization

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Towards Reusable Building Blocks to Develop COVID-19 Simulation Models

Towards Reusable Building Blocks to Develop COVID-19 Simulat...

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Simulation Winter Conference

作者： Shane A. Schroeder Christopher Vendome Philippe J. Giabbanelli Alan M. Montfort Department of Computer Science & Software Engineering Miami University Oxford OH USA IMT Mines Ales Alès FRANCE

ISBN: (纸本)9781665476621

Modeling & Simulation has played an essential role in supporting the decision-making activities of policymakers for COVID-19. However, a proliferation of models has been noted in the literature, and new models are only more likely to emerge given the shift to long-term management of the disease and the call for highly tailored tools. Having a multiplicity of models can have benefits, for example when contributing to ensembles of models. However, if each model is created from scratch, there is significant redundancy in efforts hence time inefficiency and a heightened risk of bugs. Our study examines the naturally occurring practices of modelers who wrote COVID-19 models in NetLogo to identify redundancy in code and thus suggest reusable ‘building blocks’ that would speed-up the process of model development as well as improving code quality. Based on 28 models, we identified five themes and discussed their transformation into potential building blocks for simulation.

关键词： COVID-19 Pathogens Codes Pandemics Redundancy Decision making computer bugs

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