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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, Gaurav Girisha, G.S. Shamanth, N. Dayananda Sagar University School of Engineering Department of Computer Science and Engineering Bengaluru India Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350313413

Discovering an optimal routing in Software Defined Networks (SDNs) is challenging due to several factors like scalability issues, interoperability, reliability, poor configuration of controllers and security measures. The compromised SDN controller attacks at the control plane layer, packet losses in the topology and end-to-end delay are the most security risk factors in SDNs. To overcome this, in most of the existing researches, Deep Reinforcement Learning (DRL) algorithm with various optimization techniques was implemented for optimal routing in SDN by providing link weights to balance the end-to-end delay and packet losses. DRL used Deterministic Policy Gradient (DPG) method which acts as an actor-critic reinforcement learning agent that searches for an optimal policy to minimize the expected cumulative long-term reward. However, discovering an optimal routing with efficient security measures is still a major challenge in SDNs. This research proposes a detailed review of routing optimization algorithms in SDN using Deep Learning (DL) methods which supports the researchers in accomplishing a better solution for future research. © 2023 IEEE.

关键词： Controller attacks Deep Learning Deterministic Policy Gradient Optimal routing Software defined networking

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Empowering Students and Enabling Accessibility- A survey on Personal Voice Assistant for Students Education and Enhanced Accessibility 15

Empowering Students and Enabling Accessibility- A survey on ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Yadav, Shreya Dhingra, Gauri Gupta, Sarang Paulraj, Betty Department of Computer Science and Engineering Amity School of Engineering and Technology Uttar Pradesh Noida India

ISBN: (纸本)9798331300579

Personal voice assistants have revolutionized how people interact with technology by enabling effortless access to a variety of services by making the user experience natural and more effective using Artificial Intelligence (AI) and Natural language processing (NLP) technologies to understand spoken instructions and reply the information with the most appropriate information or actions that are appropriate to the current context. The voice assistant can now be the ultimate time saver thanks to its wide range of functionalities, which range from basic tasks like setting alarms and playing music on YouTube to more advanced ones like understanding text through paragraphs and responding to any questions inside it using NLP. Personal voice assistants have shown great promise in changing human-computer interaction and promoting a more comfortable, personalized, and natural experience with computing. © Grenze Scientific Society, 2024.

关键词： Students

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Automated Tuning of Machine Learning Parameters Using Quantum Evolutionary Algorithms 1

Automated Tuning of Machine Learning Parameters Using Quantu...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Rawat, Gourav Kumar, Sandeep Kalita, Saptadeepa Sharda University School of Engineering and Technology Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350356816

Optimizing machine learning (ML) models often involves tuning various hyper parameters to achieve optimal performance. Traditional methods for optimization, such as grid search and random search, are not always effective in navigating the extensive space of hyperparameters. Recently, evolutionary algorithms (EAs) have emerged as a promising tool for streamlining the hyperparameter tuning process. Yet, the searching efficiency of traditional EAs might fall short in the face of highly intricate optimization scenarios. This innovative approach for the automated adjustment of ML hyperparameters via Quantum Evolutionary Algorithm (QEA) incorporates quantum-based genetic mechanisms such as quantum crossover and mutation to refine a pool of possible solutions. The efficacy of this methodology has been validated through tests on benchmark data sets and across various ML architectures, including neural networks, support vector machines, and decision trees. When compared with traditional optimization strategies and classical EAs, the QEA demonstrates a clear advantage in terms of the speed of convergence and the quality of the solutions found. Additionally, the scalability of this method and its applicability to practical ML challenges are discussed. © 2024 IEEE.

关键词： Quantum computers

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Video Quality Indicators for Video-Based Learning system in Higher Education 23

Video Quality Indicators for Video-Based Learning System in ...

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9th International Conference on Frontiers of Educational Technologies, ICFET 2023

作者： Zulkarnain, Novan Prabowo, Harjanto Gaol, Ford Lumban Isa, Sani Muhamad Computer Science Department BINUS Graduate Program Computer Science Information System Bina Nusantara University Indonesia Department School of Information System Bina Nusantara University Indonesia Management Department BINUS Business School Undergraduate Program Information System Department School of Information System Bina Nusantara University Indonesia

ISBN: (纸本)9798400707353

Higher education worldwide has adopted Video-Based Learning (VBL) over the past decade. They have tried to build a VBL system to improve services to students. However, the researcher's topic was not fully explored, especially in making the video itself. What features are mandatory and must be considered in making a video lecture to increase student engagement. A systematic review of the literature (SLR) is a way to identify these features. This study provides a critical analysis of the current research in VBL over the last five years (2017 - 2021). It presents a general trend examining 1336 journals and many literary works related to VBL design in Higher Education, selected through a stepwise process based on PRISMA Framework guidelines, using pre-defined selection criteria. We carefully selected 32 journals and found 22 features referring to the Lecture Video Design succession. © 2023 ACM.

关键词： Learning systems

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Enhancing pre-trained models for text summarization: a multi-objective genetic algorithm optimization approach

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Multimedia Tools and Applications 2024年 1-17页

作者： Mohan, G. Bharathi Kumar, R. Prasanna Elakkiya, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Birla Institute of Technology and Science Dubai345055 United Arab Emirates

Pre-trained language models have significantly advanced text summarization by leveraging extensive pre-training data to enhance performance. Many cutting-edge models undergo an initial pre-training phase on a large corpus before being fine-tuned specifically for text summarization tasks. The challenge arises when the limited data available for these summarization tasks and the high complexity of the models lead to aggressive fine-tuning, which can cause the models to over fit the training data and fail to generalize well to new, unseen data. To address these challenges, the study proposes a multi-objective based Genetic Algorithm (MOGA) approach for optimizing pre-trained models for summarization The proposed approach first compares the performance of various pre-trained models by fine-tuning them with the CNN/Daily Mail dataset. Secondly, the proposed Multi-Objective Genetic Algorithm (MOGA) is applied to all validated pre-trained models and compared using a statistical approach. Experimental results reveal that Pre-training with Extracted Gap-sentences for Abstractive Summarization Sequence-to-sequence (PEGASUS) model performs best among the pre-trained models. Further optimization with the proposed MOGA method yields even better results, with the optimized model achieving a Recall-Oriented Understudy for Gisting Evaluation) ROUGE-1 score of 0.514, a ROUGE-2 score of 0.4, and a ROUGE-L score of 0.5. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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User Interface and User Experience Design Methodologies and Evaluation Techniques: A systematic Literature Review 1

User Interface and User Experience Design Methodologies and ...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Raj, Gaurav Chakraverti, Ashish Kumar Mehta, Shruti N. Sharda University Department of Computer Science & Engineering School of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350356816

Effective User Interface (UI) and User Experience (UX) design are essential for digital applications, crucial for enhancing user interactions and engagement. Developing a high-quality UI is now a key aspect of software development. This study presents a comprehensive literature review of recent research on UI and UX design methodologies for digital products. It serves as the preliminary findings for an ongoing research project aimed at using this knowledge to develop a mobile game. The qualitative analysis insights are expected to significantly improve user interaction and experience in digital applications. © 2024 IEEE.

关键词： User centered design

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Exploring Eye Gaze Patterns in Three Dimensions: An Innovative Visualization Dashboard 2

Exploring Eye Gaze Patterns in Three Dimensions: An Innovati...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Akshay, S. Yashwanth, M.A. Manohar, N. Jipeng, Tian Amudha, J. School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Karnataka Mysore India Computer Science College Zhongyuan University of Technology Department of Computer Science China School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350383652

Eye tracking is a method of measuring and analyzing the movements of the eyes as they move and focus on different objects in the visual field. This technique has been used in a wide range of fields, including psychology, neuroscience and human-computer interaction. Eye tracking provides valuable insights into how individuals process visual information, including attentional biases, cognitive processing, and decision-making. Traditionally, eye tracking has relied on licensed software and specialized hardware, making it costly and less accessible for some researchers and applications. However, with recent technological advancements and by leveraging the capabilities of webcams, researchers, and practitioners can now perform eye-tracking experiments without the need for expensive equipment. By utilizing webcams, researchers can conduct eye-tracking experiments in a cost-effective manner, making the method more accessible to a broader range of researchers and practitioners. © 2024 IEEE.

关键词： Human computer interaction

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Harmony in transcripts: a systematic literature review of transcriptome-wide association studies

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Journal of engineering and Applied science 2024年第1期71卷 1-15页

作者： Mashhour, Mahinaz A. Kandil, Ahmed Hisham AbdElwahed, Manal Mabrouk, Mai S. Biomedical Engineering Department Misr University for Science and Technology Giza Egypt Systems and Biomedical Engineering Department Cairo University Giza Egypt School of Information Technology and Computer Science Nile University Sheikh Zayed City Egypt

Transcriptome-wide association studies (TWAS) goal is to better understand the etiology of diseases and develop preventative and therapeutic approaches by examining the connections between genetic variants and phenotypes while overcoming the limitations of the genome-wide association study (GWAS). It is a valuable complement to GWAS, reducing the negative effects of multiple tests and enabling a more thorough investigation of gene expression patterns in various tissues. A systematic review is presented in this paper to identify articles that utilize TWAS to understand the genetic factors behind complex diseases. A detailed selection process was carried out using standard PRISMA criteria to select relevant articles for the review. Twenty-five articles passed the inclusion criteria and were selected for additional review. The studies cover a diverse range of disorders, including Tourette’s syndrome, Alzheimer’s disease, rheumatoid arthritis, and major depression. Leveraging gene expression data from different tissues and populations, these investigations successfully identified novel genes and pathways associated with the studied conditions. The collective findings highlight the transformative impact of integrative genomics in advancing our understanding of complex diseases, providing insights into potential therapeutic targets, and laying the foundation for precision medicine approaches. © The Author(s) 2024.

关键词： Genome

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Hybrid Automated Malware Detection for Android User Data with Optimal Learning Approach 2

Hybrid Automated Malware Detection for Android User Data wit...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Kumar, Mysugari Pradeep Chouhan, Arun Singh Department of Computer Science and Engineering School of Engineering Malla Reddy University Telangana Hyderabad India

ISBN: (纸本)9798350383867

The Android operating system is currently the most popular and widely used smartphone operating system. One reason for their appeal is that users can download and install free third-party programs that offer a number of benefits. Unfortunately, installing any third-party program has also led to an endless supply of malware programs that are always evolving and intended to harm users in different ways. This paper addresses and demonstrates a number of techniques to the test of Android malware detection. For a realtime detection system, data analytics are created. The installed applications may be searched by the detection system for ones that might be dangerous so they can be removed. Using machine learning models allows for this. The Automatic Android Malware Detection utilizing Optimal Ensemble Learning Approach for Cybersecurity (AAMDOELAC) technique is presented in this research proposal. Automated malware identification and categorization is the primary goal of the AAMD-OELAC approach. The AAMDOELAC approach does preliminary data preparation to accomplish this. The three machine learning models implemented in the ensemble learning phase of the AAMDOELAC methodology are the Regularized Random Vector Functional Link Neural Network (RRVFLN), Kernel Extreme Learning Machine (KELM), and Least Square Support Vector Machine (LSSVM). We utilize the Chi Square Model to extract data. The simulation findings show that the AAMD-OELAC technology outperforms other existing techniques. © 2024 IEEE.

关键词： Android malware

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Semantic Analysis Using Deep Learning for Predicting Stock Trends 2

Semantic Analysis Using Deep Learning for Predicting Stock T...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Galphade, Manisha Nikam, V.B. Yedurkar, Dhanalekshmi Singh, Prabhishek Stephan, Thompson Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India Department of Computer Engineering & IT Veermata Jijabai Technological Institute Mumbai India Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India

Company Investors and financial professionals mostly rely on quarterly reports to help them decide the ways to invest in stocks and assess the company's current performance. Quarterly company reports offer an abstracted perspective of the company's overall past performance, as well as its present situation and the market value of its market share. Financial text streams in quarterly report are unstructured naturally, but they represent cooperative expressions that are of important in any financial decision for stake holder. It will be both daunting and necessary to procedure intelligence of unstructured textual data. In this study, we address important queries related with the explosion of interest in a method to extract useful information from unstructured data and the way to work out if such insight provides any hints regarding the trends of financial markets. There is a lack of availability in the labeled dataset for financial sentiment analysis applications. The pre-trained language model employs very little labeled parameters that is used for a variety of domain specific corpora including financial sentiment analysis. In this paper, FinBERT, a model built on the BERT framework, to address linguistics challenges in the financial domain. The proposed work uses twelve transformer layers and twelve attention layers with several million parameters. The design of encoder and decoder comprises of several attention layers along with RNN. This arrangement aids to recognize instances processing the strongest relation between the words within a particular sentence. The experimentation results shows that the presented method surpass the state-of-the-art methods for financial datasets. The results are also compared with other existing models using the same financial dataset. It is observed that the FinBERT attains an accuracy of 84.77% on quarterly reports despite using a lesser training set. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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