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2nd International Workshop on Intelligent Software engineering, ISE 2023

作者： Ha, Yu-Jin Kim, Gun-Woo Department of AI Convergence Engineering Gyeongsang National University Jinju Korea Republic of School of Computer Science Department of AI Convergence Engineering Gyeongsang National University Jinju Korea Republic of

Recently, there has been a growing trend of consumers seeking product information from video platforms such as YouTube. However, when viewing multiple review videos about the same product, viewers often encounter redundant information, resulting in wasted time. To address these issues, we use BERTopic to eliminate repetitive video content and address the problem of missing subtopics, which has been a limitation of traditional video summarization methods. Subsequently, the topic-aware video contents are summarized using the BART model. The ROUGE metric was used to evaluate the model proposed in this paper, and the experimental results showed improved results compared to previous research. © 2023 Copyright for this paper by its authors.

关键词： Video recording

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Collaborative and Integrated Platform to Support Distributed Manufacturing System Using a Service-Oriented Approach Based on Cloud Computing Paradigm

Collaborative and Integrated Platform to Support Distributed...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Nainwal, Ankita Sharma, Vishal Varshney, Neeraj Shirish Boob, Nandini Reddy, N.V. Uma Reddy, K. Jyothsna Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India GLA University Department of Computer Engineering and Applications Mathura India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Bangalore India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

Increasing interest in distributed manufacturing systems is a result of the dynamic nature of today's business environment and with that comes the popularity of their use. This has given rise in the interest in distributed manufacturing systems. The indirect consequence of this has led to much higher use of distributed manufacturing systems. A large number of production processes, dispersed across a multitude of locations geographically separate, must be controlled and integrated in order to achieve production agility, flexibility and efficiency of costs. In our most recent project, we developed an integrated, collaborative manufacturing platform, the centerpiece of which was LAYMOD. To address the issues posed by such systems, this research presents a collateral and integrated platform based on a service oriented approach that can operate within the cloud computing paradigm. These systems present problems which the study is conducted in order to solve. Utilising the features of cloud computing, it aims to serve as a platform in solving process coordination and hence improving efficiency and scalability of distant manufacturing processes and potential real time communications among various stake holders. Furthermore, statistical research on the rates of platform module adoption in different settings provides specifics of (a) use patterns of some platform modules and (b) what capabilities are provided by some platform modules. When extrapolated to a wider size, our work is applied towards the creation of collaborative and integrated platforms for a number of the various manufacturing processes which are currently in use. © 2025 IEEE.

关键词： Cloud platforms

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Social engineering attack types and prevention techniques- A survey 1

Social engineering attack types and prevention techniques- A...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Bhatia, Shruti Yadav, Satya Prakash Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

The term "Cyber Security"is getting more and more popular and important over the last few years. Since computers and most of the devices are connected to the internet, they are likely to be hacked by the hackers. In the past, this issue was not a big problem, because not every device is required to be connected to the internet or the internet was not popular as today. But the case has changed over the years. All the changes in the technology area also changed the cyber-attack models as well. With the development of technology and the change of usage of the internet over the last years cyber-attacks have become more common and popular by the hackers. Hackers have discovered that they have an opportunity to steal or earn money in a short time without having to spend too much effort. These days, the type of cyber-attacks is not the same as the ones in the past. As time passes, the cyber-attack methods are also changing and evolving. Today, hackers are using more advanced and effective cyber-attack methods compared to past years. There are many methods that are impossible to cover all of them in this article. Our main focus will be on social engineering attacks in this article. Social engineering attacks use different approaches to cyber attacking. Unlike trying to explode a victim's social media password etc. using advanced exporting programs, algorithms or techniques, social engineering attacks focus on fooling victims into providing their data to hackers by themselves without using or implementing any password cracking, exploiting techniques etc. We will go over what social media engineering is, type of social engineering methods, what countermeasures can be used to protect from social engineering and more in this article. © 2023 IEEE.

关键词： Crime

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QuMIN: quantum multi-modal data fusion for humor detection

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Multimedia Tools and Applications 2025年第18期84卷 18855-18872页

作者： Phukan, Arpan Haq Khan, Anas Anwarul Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta Bihar Patna801106 India Kalinga Institute of Industrial Technology Odisha Bhubaneswar751024 India School of AI and Data Science Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India

Humour detection has attracted considerable attention due to its significance in interpreting dialogues across text, visual, and acoustic modalities. However, effective methods to map correlations among different modalities remain an active area of research. In this study, we go beyond traditional machine learning techniques by introducing a Variational Quantum Circuit (VQC) that capitalizes on the inherent quantum properties of superposition, entanglement, and interference. Our proposed model, Quantum Multi-Modal Data Fusion (QuMIN), is designed to better capture and reproduce the interaction across modalities, as well as the internal correlations within each modality. Our introduction of the novel VQC, which augments the DialogueRNN baseline with only an additional 4,809 parameters, signifies a substantial advancement in multi-modal humor detection with improvements of 12.34% in precision, 8.84% in recall and 10.57% in F1 score compared to the state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Machine learning

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ai-Powered Object Detection for Autonomous Vehicles: A Comparative Study of Machine Learning Models

AI-Powered Object Detection for Autonomous Vehicles: A Compa...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Shrivastava, Anurag Kansal, Vipashi Nagpal, Amandeep Manjunatha Dixit, Krishna Kant Rajkumar, K. Varada Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India GLA University Department of Electrical Engineering Mathura India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

Object detection is an integral piece of autonomous cars learning their surroundings and making safe judgements about where and how to drive in real time. This research compares and contrasts multiple machine learning models for object identification in autonomous cars in order to assess accuracy, computational economy, and real time performance. Then, we present how various well known proposed models (CNN, YOLO, SSD, R-CNN) perform in changing driving conditions. It researches the models capabilities to run real time applications within autonomous cars, as well as the trade off of speed and accuracy between different models. We also study how hyperparameter adjustment, data augmentation and transfer learning impact these models' performance. Through thorough research on benchmark datasets, this work shows the benefits of the adoption of hybrid models, which mix speed and accuracy, to increase item recognition in real world contexts. This study contributes to the expanding corpus of knowledge on ai driven solutions for autonomous systems by having useful insights regarding the improvement of autonomous vehicle object identification algorithms. © 2025 IEEE.

关键词： Contrastive Learning

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Design and Evaluation of IoT Prototypes: Leveraging Test-Beds for Performance Assessment and Innovation

Design and Evaluation of IoT Prototypes: Leveraging Test-Bed...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Kotiyal, Arnav Shrivastava, Anurag Nagpal, Amandeep Manjunatha Dixit, Krishna Kant Reddy, R. Akhilesh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Lovely Professional University Phagwara India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India GLA University Department of Electrical Engineering Mathura India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

When you mention the Internet of things (IoT), connected devices and systems can be made seamless. Whilst creating and prototyping IoTs are oftentimes necessary, design, testing and evaluating IoT prototypes require robust frameworks and methodologies to achieve reliable performance and innovation. In this paper, test beds as controlled environments for performance assessment of IoT prototypes are discussed and these tested in terms of design and evaluation. Of importance are test-beds as they aid in simulating real world conditions to do iterative improvements, and foster innovation in IoT solutions. It presents some key methodologies for developing IoT prototypes, underlines the role of test beds in the validation process, and reports about evaluation of some case studies demonstrating the impact of such frameworks on alleviating inefficiency, unreliability, and non-scalability. The paper combines leading edge technologies with strong data driven decision making to present actionable feedback on exploiting test beds to catalyse IoT innovation against pivotal challenges including interoperability, security, and scalability. © 2025 IEEE.

关键词： Testbeds

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Advanced Fuzzy Rule Set-Based Approach for Fetal Heart Rate Detection and Classification 4

Advanced Fuzzy Rule Set-Based Approach for Fetal Heart Rate ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Ambilwade, R.P. Giri, Neeraj Nagaraju, Regonda Gupta, Vikas Kommina, Subhash Bhagavan Mishra, Devanshi National Defence Academy Department of Computer Science Pune India Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Malla Redy University School of Engineering Department of Ai & Ml Telangana Hyderabad India Gyan Ganga Institute of Technology & Sciences Dept of CSE- Aiml Jabalpur India It Department Andhra Pradesh India Ggits MP Jabalpur India

ISBN: (纸本)9798331518981

Fetal heart rate measurement is important in evaluation of fetal well-being and the visible sign of compromise or dysfunction. Congenital heart diseases rank among the main causes of infant mortality and can be identified by means of fetal heart rate monitoring. An effective and reliable method of recognizing and categorizing fetal heart rate is hence important for correct diagnosis and early intervention. The directed research utilizes an improved fuzzy rule set to boost fetal heart rate detection and classification precision. This technique incorporates fuzzy algorithms to adapt to the non-linearity of the fetal cardiotocographic signal as compared to precise methodologies. Some of the difficulties that arise when using FHR analysis include handling of maternal heart rates, power line interference and other biomass signals which tend to complicate the fetal signal. With fuzzy rule sets, this work shall be able to generate better classification between fetal and maternal components for accurate extraction and differentiation of FHR patterns. Moreover, this approach is able to account for uncertainties and temporal changes of the signal and could potentially provide a more robust solution for clinical applications of fetal monitoring and early detection of congenital heart diseases. © 2024 IEEE.

关键词： Fetal monitoring

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Towards efficient generative ai and beyond-ai computing:New trends on ISSCC 2024 machine learning accelerators

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Journal of Semiconductors 2024年第4期45卷 12-15页

作者： Bohan Yang Jia Chen Fengbin Tu Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong KongChina AI Chip Center for Emerging Smart Systems The Hong Kong University of Science and TechnologyHong KongChina School of the Gifted Young University of Science and Technology of ChinaHefei 230026Chin

Compared to the last decade when the convolution neu-ral network(CNN)dominated the research field,machine learn-ing(ml)algorithms have reached a pivotal moment called the generative artificial intelligence(ai)*** the emer-gence of large-scale foundation models[1],such as large multi-modal model(LMM)GPT-4[2]and text-to-image generative model DALL·E[3].

关键词： ISSCC beyond ai

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Advanced Machine Learning Techniques for Fake News Detection: A Comprehensive Analysis Using the LIAR Dataset 3rd

Advanced Machine Learning Techniques for Fake News Detectio...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, mlCCIM 2024

作者： Paul, Subrata Ghosh, Shivnath Mitra, Anirban Bhattacharya, Pronaya Department of CSE-AI Brainware University West Bengal Barasat India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University West Bengal Kolkata India Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University West Bengal Kolkata India

ISBN: (纸本)9789819624676

In today’s digital world, fake news presents a serious challenge to social cohesiveness, trust among individuals, and the functioning of democracy. Overcoming this issue demands novel solutions that make efficient use of machine learning (ml) for identifying and battle disinformation. This paper examines the important problem of fake news, including its meaning, its consequences, and the significance of social media in its rapid spread. The study emphasises the complexities of identifying fake news, focusing on sophisticated techniques like ai-generated content and the widespread dissemination of misinformation. In order to tackle such obstacles, the paper uses ml approaches, particularly the LIAR dataset and the XGBoost model, to create a reliable fake news detection system. The outcomes show that these methods have been successful at successfully recognising false information, highlighting ml’s prospective in reducing misinformation. This research contributes to the broader discourse on media literacy and the need for reliable information in the digital age. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Human Age Prediction Based on Brain MRI Using Density-Based Regression

Human Age Prediction Based on Brain MRI Using Density-Based ...

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2023 IEEE International Conference on Industrial Technology, ICIT 2023

作者： Bregu, Ornela Zamzami, Nuha Bouguila, Nizar Concordia Institute for Information Systems Engineering Concordia University Montreal Canada College of Computer Science and Engineering University of Jeddah Department of Computer Science and Ai Jeddah Saudi Arabia

ISBN: (纸本)9798350336504

In this paper, we have applied a density-based regression technique to predict the age of a human brain based on Magnetic Resonance Images. We propose four different multivariate discrete distributions to accommodate different mean-covariance and correlation structures, given the challenging nature of medical datasets. Then, we use a novel Minorization-Maximization framework to address the issue of high dimensional optimization. Finally, to handle the time and memory requirements of processing large amounts of high-resolution images, we have adopted a real-time framework, where the learning behavior is gradually improved as new data become available. © 2023 IEEE.

关键词： Forecasting

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