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41st International Conference on Machine Learning, ICML 2024

作者： Shubham, Kumar Jayagopal, Aishwarya Danish, Syed Mohammed Prathosh, A.P. Rajan, Vaibhav Department of Electrical Communication Engineering Indian Institute of Science Karnataka Bengaluru India Department of Information Systems and Analytics School of Computing National University of Singapore Singapore Indian Institute of Technology Bihar Patna India

Cancer, a leading cause of death globally, occurs due to genomic changes and manifests heterogeneously across patients. To advance research on personalized treatment strategies, the effectiveness of various drugs on cells derived from cancers (‘cell lines') is experimentally determined in laboratory settings. Nevertheless, variations in the distribution of genomic data and drug responses between cell lines and humans arise due to biological and environmental differences. Moreover, while genomic profiles of many cancer patients are readily available, the scarcity of corresponding drug response data limits the ability to train machine learning models that can predict drug response in patients effectively. Recent cancer drug response prediction methods have largely followed the paradigm of unsupervised domain-invariant representation learning followed by a downstream drug response classification step. Introducing supervision in both stages is challenging due to heterogeneous patient response to drugs and limited drug response data. This paper addresses these challenges through a novel representation learning method in the first phase and weak supervision in the second. Experimental results on real patient data demonstrate the efficacy of our method WISER (Weak supervISion and supErvised Representation learning) over state-of-the-art alternatives on predicting personalized drug response. Our implementation is available at https://***/kyrs/WISER. Copyright 2024 by the author(s)

关键词： Self-supervised learning

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DeepDect: A Facial Deepfake Video Detection Application using Ensemble Learning 12

DeepDect: A Facial Deepfake Video Detection Application usin...

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12th IEEE Conference on systems, Process and Control, ICSPC 2024

作者： Tay, Wan Xin Chua, Hui Na Jasser, Muhammed Basheer Issa, Bayan Wong, Richard T.K. Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Selangor Darul Ehsan Bandar Sunway47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syria

ISBN: (纸本)9798350391398

The emerging technology of deepfake video poses significant threats to information integrity and public trust. Deepfake videos come in various forms, including face swaps, lip-syncing, and full-body simulations. Detection algorithms may struggle to generalize across these different types of deepfakes, leading to lower effectiveness in real-world applications. An advanced deepfake detection system is developed using an ensemble approach, combining the strengths of three pre-trained Convolutional Neural Networks (CNNs)ResNeXt50, Xception, and EfficientNet-v2-with Long ShortTerm Memory (LSTM) layers to capture both spatial and temporal features from videos to improve detection accuracy. The ensemble model is further enhanced by incorporating additional features such as lip movements, eye blinks, head poses, and color contrasts to identify subtle inconsistencies in deepfake videos that are often ignored by traditional detection methods. The ensemble model achieves an accuracy of 84.06%, with a good balance between precision and recall, indicating its effectiveness in real-world applications. We developed DeepDect, a web application embedded with the ensemble model, to provide a user-friendly interface for deepfake detection by uploading videos or submitting URL links. The application includes educational content about deepfake techniques to raise user awareness about the potential dangers of deepfakes. © 2024 IEEE.

关键词： Convolutional neural networks

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Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

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Computer systems Science & Engineering 2023年第12期47卷 2953-2969页

作者： Ehab Bahaudien Ashary Sahar Jambi Rehab B.Ashari Mahmoud Ragab Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

Object Detection is the task of localization and classification of objects in a video or *** recent times,because of its widespread applications,it has obtained more *** the modern world,waste pollution is one significant environmental *** prominence of recycling is known very well for both ecological and economic reasons,and the industry needs higher *** object detection utilizing deep learning(DL)involves training a machine-learning method to classify and detect various types of waste in videos or *** technology is utilized for several purposes recycling and sorting waste,enhancing waste management and reducing environmental *** studies of automatic waste detection are difficult to compare because of the need for benchmarks and broadly accepted standards concerning the employed data ***,this study designs an Entropy-based Feature Fusion using Deep Learning forWasteObject Detection and Classification(EFFDL-WODC)*** presented EFFDL-WODC system inherits the concepts of feature fusion and DL techniques for the effectual recognition and classification of various kinds of waste *** the presented EFFDL-WODC system,two major procedures can be contained,such as waste object detection and waste object *** object detection,the EFFDL-WODC technique uses a YOLOv7 object detector with a fusionbased backbone *** addition,entropy feature fusion-based models such as VGG-16,SqueezeNet,and NASNetmodels are ***,the EFFDL-WODC technique uses a graph convolutional network(GCN)model performed for the classification of detected waste *** performance validation of the EFFDL-WODC approach was validated on the benchmark *** comprehensive comparative results demonstrated the improved performance of the EFFDL-WODC technique over recent approaches.

关键词： Object detection object classification waste management deep learning feature fusion

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Evolution of Web API Cooperation Network via Exploring Community Structure and Popularity

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6659-6671页

作者： Kang, Guosheng Wang, Yang Liu, Jianxun Cao, Buqing Xiao, Yong Xu, Yu Hunan University of Science and Technology Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China University of Cambridge Cambridge Baker Systems Genomics Initiative British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care CambridgeCB1 8RN United Kingdom

With the growing popularity of the Internet, Web applications have become increasingly essential in our daily lives. Web application programming interfaces (Web APIs) play a crucial role in facilitating interaction between applications. However, most Web service platforms are suffering from the imbalance of Web services now, many services of good quality but low popularity are difficult to be invoked even once and do not create direct connections with the users. Some graph-based Web service recommendation methods also often present a long-tailed distribution of recommended Web services due to limited Mashup-API invocation relationships. To relieve this problem and promote service recommendation, in this article, we propose a community structure and popularity-based approach by constructing an evolving cooperation network for Web APIs. We leverage the Louvain algorithm in community detection to assign community structure to each Web API and consider both the popularity and community structure in constructing the network. By optimizing the Barabαsi-Albert (BA) evolving network model, we demonstrate that our approach outperforms the BA, Bianconi-Barabαsi (BB), and popularity-similarity optimization (PSO) models in Web service clustering. Based on our proposed evolutionary network model for the evolutionary extension of API cooperation network and used for downstream Web service recommendation tasks, the experimental results also show that our recommended approach outperforms some other baseline models for Web service recommendation. © 2020 IEEE.

关键词： Web services

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In This Special Issue: Data Breaches in the Cloud—Business Security and Risk Management

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2025年第1期6卷 2-14页

作者： Kolevski, David Michael, Katina Freeman, Mark University of Wollongong School of Computing and Information Technology WollongongNSW2500 Australia Newcastle University School of Business Newcastle upon TyneNE1 4SE United Kingdom Arizona State University School for the Future of Innovation in Society School of Computing and Augmented Intelligence TempeAZ85281 United States The University of Sydney Business School Business Information Systems Discipline NSW Sydney2006 Australia

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LPT-One and BFD-One Search Algorithms for Load Balance and Bin-Packing of Cloud computing

LPT-One and BFD-One Search Algorithms for Load Balance and B...

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IEEE International Conference on Cloud computing and Intelligence systems (CCIS)

作者： Guangyao Zhou Wenhong Tian Rajkumar Buyya School of Information and Software Engineering University of Electronic Science and Technology of China China School of Computing and Information Systems The University of Melbourne Australia

Cloud computing contains large-scale tasks and resources. Currently, the local search is a considerable choice in ensuring both computational complexity and optimization. Based on our previous research on multi-route search algorithm to reduce makespan, we apply BFDO and LPTO algorithms to address load balancing and bin-packing problems. Through abundant experiments, we validate the superiority of our proposed BFDO and LPTO.

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Deep reinforcement learning-based algorithms selectors for the resource scheduling in hierarchical Cloud computing

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Journal of Network and Computer Applications 2022年第0期208卷

作者： Zhou, Guangyao Wen, Ruiming Tian, Wenhong Buyya, Rajkumar School of Information and Software Engineering University of Electronic Science and Technology of China China Cloud Computing and Distributed Systems Lab School of Computing and Information Systems The University of Melbourne Australia

Cloud computing environment is becoming increasingly complex due to its large-scale information growth and increasing heterogeneity of computing resources. Hierarchical Cloud computing dividing the system into multi-levels with multiple subsystems to support the adaptability to abundant requests from users has been widely applied and brings great challenges to resource scheduling. It is critical to find an effective way to address the complex scheduling problems in hierarchical Cloud computing, whose scenarios and optimization objectives often change with the types of subsystems. In this paper, we propose a scheduling framework to select the scheduling algorithms (SFSSA) for different scheduling scenarios considering no algorithm well suitable to all scenarios. To concretize SFSSA, we propose deep learning-based algorithms selectors (DLS) trained by labeled data and deep reinforcement learning-based algorithms selectors (DRLS) trained by feedback from dynamic scenarios to complete the algorithms selection regarding the scheduling algorithms as selectable tools. Then, we apply strategies including pre-trained model, long experience reply and joint training to improve the performance of DRLS. To enable the quantitative comparison of selectors, we introduce a weighted cost model for the trade-off between solution and complexity. Through multiple sets of experiments in hierarchical Cloud computing with multi subsystems for five types of scheduling problems and varying weights of cost, we demonstrate DLS and DRLS outperform baseline strategies. Compared with random selector, greedy selector, round-robin selector, single best selector, virtual best selector and single fast selector, DLS reduces the cost by 47.4%, 46.1%, 33.9%, 47.9%, 19.3%, 18.8% under stable parameter ranges, and DRLS reduces the cost by 41.1%, 40.6%, 11.7%, 42.3%, 11.5%, 12.5% in dynamic scenarios respectively. In experiments, we also validate DRLS has stronger adaptability than DLS in dynamic schedulin

关键词： Reinforcement learning

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AI-Driven Virtual Mock Interview Development 13

AI-Driven Virtual Mock Interview Development

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Joint 13th International Conference on Soft computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Mishra, Prabhat Kumar Arulappan, Arun Kumar Ra, In-Ho Thanga Mariappan, L. Gina Rose, G. Lee, Young-Seok School of Computer Science Engineering and Information Systems Vit University Vellore India School of Computer and Software Kunsan National University Gunsan Korea Republic of Anna University Department of Computer Technology Chennai India

ISBN: (纸本)9798350373332

The integration of Artificial Intelligence (AI) into educational technologies marks a significant shift in learning methodologies and operational dynamics within educational institutions. At the forefront is an AI-driven virtual mock interview platform designed to address the high Customer Acquisition Costs (CAC) in the edtech sector, especially for interview preparation services. This initiative harnesses a blend of AI technologies, including ADA 2 for creating context-aware embeddings and Machine Learning (ML), to transform the traditional mock interview process into a dynamic, cost-effective system. Central to the platform is its use of advanced Natural Language Processing (NLP) techniques and GPT-4 Large Language Model (LLM), automating the process of mock interviews and providing personalized feedback, ensuring a preparation journey that meets specific candidate needs and mirrors real interview scenarios. A key evaluation among 100 students from a cohort of 1800 demonstrated a 90% cost reduction for three mock interviews, reducing expenses from 3000 to just 300 per candidate. This cost efficiency significantly enhances access to quality interview preparation, improving student satisfaction and accessibility. Moreover, the platform provides valuable insights into student performance, setting a new standard in educational technology by offering an effective, personalized interview preparation experience. This project reflects a holistic approach to student development and the critical role of technology in addressing the evolving needs of learners. © 2024 IEEE.

关键词： Cost reduction

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Transformer-Based Model for the Natural Language Textual Sequences to SQL-Queries Conversion

Transformer-Based Model for the Natural Language Textual Seq...

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International Computer Sciences and information Technologies (CSIT)

作者： Kateryna Yalova Andrii Zhuravel Department of Information Systems Application University of Information Technology and Management Rzeszow Poland Systems Software Department Dniprovsky State Technical University Kamianske Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

The concept of using natural language instead of SQL queries formed the basis of a new type of data processing called Natural Language Interface to Database (NLIDB). This paper considers a neural network (NN) approach for the NLIDB systems development. Its essence is the transformation of the input text sequence specified by the user in natural language into commands in SQL, which is inherent in text-to-SQL and sequence-to-sequence tasks. The Transformer model architecture of the NN was justified in the research. This architecture allows the NN to convert an input sequence (such as a natural language text query) into an output sequence (translated into SQL format text query). WikiSQL dataset was used for NN learning and testing. Numerous experiments were conducted in order to determine the optimal parameters of the NN model were conducted. Bilingual Evaluation Understanding (BLEU) metric was used as a metric for evaluating the quality of converting an input text sequence to SQL queries. The BLEU metric for the neural network model trained on the WikiSQL sample is 56%.

关键词： Measurement Structured Query Language Accuracy Translation Computational modeling Artificial neural networks Computer architecture Transformers Vectors Machine translation

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A Developed Multi-Level Deep Learning Model for Network Slicing Classification in 5G Network 12

A Developed Multi-Level Deep Learning Model for Network Slic...

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12th International Conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2023

作者： Hamza, Hawraa S. Ebady Manaa, Mehdi Department of Software College of Information Technology University of Babylon Babylon51001 Iraq Department of Intelligent Medical Systems College of Health and Medical Techniques Al-Mustaqbal University University of Babylon Babylon51001 Iraq

5G is considered as a key contributor and infrastructure supplier in the communication technology industry, capable of supporting a wide range of services such as virtual reality, driverless automobiles, e-health, and a variety of intelligent applications. Network slicing is designed to support the diversity of services applications with increased performance and flexibility needs by dividing the physical network into many logical networks. Service classification allows 5G service providers to accurately select the network slices for each service. We propose a Network Slicing classifier that uses a Multi-level Deep learning Model. First, we created a dataset of 5G network slicing that contain attributes connected with various network services. Next, we performed a multi-level model that consist of a set of Machine learning and deep learning model (such as deep Neural Network, Random Forest and Decision Tree) as a first level followed by next level that which is represent Attentive Interpretable Tabular Learning model. The outcomes of the experiment showed that the proposed model was able to exceed the normal models with high performance results. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： 5G mobile communication systems

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