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Behavioral insights into compression: a study of Move-to-Front-or-Middle deterministic online algorithm through sequence classification and characterization

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International Journal of Information technology (Singapore) 2025年第1期17卷 189-203页

作者： Baisakh Mohapatra, Hitesh Guru, Abhishek Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Sambalpur India Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to Be University Bhubanseswar India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Since the list update problem was applied to data compression as an effective encoding technique, numerous deterministic algorithms have been studied and analyzed. A powerful strategy, Move-to-Front (MTF), involves moving an accessed item to the front of the list immediately. Theoretical analysis has demonstrated MTF’s potential, showing it to be 2-competitive through competitive analysis. This success has inspired researchers to develop various intriguing MTF variants and assess their performance both theoretically and experimentally. One such variant, Move-to-Front-or-Middle (MFM), has been experimentally shown that MFM performed better than the classic MTF on Calgary and Cantebury Datasets. However, the experimental performance alone does not provide sufficient insight into the internal behavior of the algorithm or explain why its performance surpasses MTF on certain datasets. Therefore, in this paper, we conduct a detailed behavioral study of MFM through sequence classification and characterization to gain deeper understanding of the algorithm and aims to reconcile the disparity between theoretical and experimental outcomes. This paper also by classifying and characterizing MFM’s performance across various request sequences, considering both the presence and absence of locality of reference. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Competitive analysis Deterministic list update algorithm List update problem Sequence classification

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Soundtrack Success: Unveiling Song Popularity Patterns Using Machine Learning Implementation

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SN computer Science 2024年第3期5卷 278页

作者： Arora, Shruti Rani, Rinkle Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Music streaming services are getting increasingly popular as a result of the prevalent need for web and smart gadgets. Melophiles are drawn toward a range of musical genres and create a unique digital footprint. The emotional reactions of the music listeners may cause physiological changes as well as neurological benefits. However, any musician should avoid being influenced by an algorithmic recommendation presented in the research. The most prominent aspect for any musician is to stay true to their own musical style and to be novel, honest, and sincere. The main objective of this research work is to provide precise music predictions to the artists before they launch their music in the market. This work also aims to improve the accuracy of the prediction models using some feature-engineering-based techniques to manipulate the Spotify dataset and predict whether the song will be a hit or not. The proposed work leverages state-of-the-art machine learning models, including Linear Regression, Lasso Regression, Ridge Regression, Elastic Net, Random Forest, GBM, and neural networks to explore the predictive factors influencing the inclusion of a particular music track in the featured Spotify Hit-50 list. Furthermore, it offers descriptive statistical analysis of the features that can help in popularity prediction. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Hit song science audio features Music information retrieval Music streaming data Regression

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Document Summarizer: A Machine Learning approach to PDF summarization 4

Document Summarizer: A Machine Learning approach to PDF summ...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Peddarapu, Rama Krishna Anogna, Sudagoni Harshini, M.N.S. Sri Sireesha, J. Rathna, K. Guna Department of Computer Science and Engineering Faculty of Computer Sciences and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India

To address the need for summarizing and extracting information efficiently, this paper highlights the growing challenge posed by the increasing number of PDF files. Reading lengthy documents is a tedious and time-consuming task. To save time and quickly comprehend the key points of a PDF, a PDF summarizer tool has been developed to tackle these issues. In today's professional environments, gathering and managing data from documents is critical. This article introduces an innovative solution called DocSum, which automates the process of summarizing extracted data. Powered by *** Core, the DocSum system allows users to upload PDF documents for processing and receive concise summaries in return. The system features a user-friendly interface that encourages interaction and engagement, utilizing AI and machine learning techniques to streamline document handling. Users can request specific summaries, enabling efficient document management workflows. By empowering users to seamlessly interact with vast amounts of information, DocSum enhances productivity and explores new ways to optimize document management. This solution is ideal for professionals seeking to stay informed and manage data effectively, while also keeping track of advancements in document handling technologies across multiple industries. It is not only helpful for professionals but also helpful for students who need quick revision or examinations, teachers to gain brief knowledge on the concepts. © 2025 The Authors. Published by Elsevier B.V.

关键词： Data handling

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Fast Gradient Sign Method (FGSM) Variants in White Box Settings: A Comparative Study 7

Fast Gradient Sign Method (FGSM) Variants in White Box Setti...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Lad, Aarti Bhale, Ruchi Belgamwar, Shlok SCTR's Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350359299

Adversarial Machine Learning is a critical research area focused on understanding the vulnerabilities of machine learning models. Adversarial attacks have the potential to significantly impact deep learning models, particularly Convolutional Neural Networks (CNNs), undermining their reliability and real-world applications. This research study analyzes the Fast Gradient Sign Method (FGSM) and its variants, examining their effects on the ResNet-50 CNN model. By leveraging the model's training on the ImageNet dataset, this study aims to reveal the mechanisms of each attack and identify their weaknesses. Through a comprehensive comparison and analysis of their behaviors, this research study aims to provide developers with a thorough understanding of FGSM's characteristics, thereby facilitating the development of robust defense strategies against adversarial threats in deep learning models. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Analysis of DCGAN and WGAN in Logo Generation 3

A Comparative Analysis of DCGAN and WGAN in Logo Generation

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Kadam, Shubhan Jani, Jay Kudtarkar, Aniket Mulla, Nikahat Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India Sardar Patel Institute of Technology Department of Computer Science Engineering Mumbai India

ISBN: (纸本)9798350375190

This research paper explores the impact of Generative Adversarial Networks (GANs) on logo generation, focusing on three variants: Deep Convolutional GAN (DCGAN), Wasserstein GAN (WGAN), and Conditional GAN (cGAN). The study evaluates their strengths and weaknesses, aiming to contribute insights to the ongoing discourse on GANs. Results indicate that DCGAN excels in capturing fine details but faces mode collapse, limiting diversity, while WGAN demonstrates enhanced training stability, consistently delivering diverse and visually appealing logos. Numerical results reveal a mean rating of 3.173/5 across sample sets, with approximately 14/64 images deemed useful, resulting in a usability rating of 2.2/10. The survey findings, based on assessments from 57 participants, consistently rate DCGAN-generated images slightly higher in terms of visual appeal and usage preferences compared to those produced by WGAN. While DCGAN exhibits superior image quality, WGAN offers more variety. This suggests a trade-off between variety and image quality, underscoring the specific strengths of each model in meeting subjective criteria for visual excellence. © 2024 IEEE.

关键词： Generative adversarial networks

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A Comprehensive Review of Swarm Intelligence-Based Meta-Heuristic Algorithms 3

A Comprehensive Review of Swarm Intelligence-Based Meta-Heur...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Suman, Abhineet Gunjan Biswas, Anupam National Institute of Technology Department of Computer Science and Engineering Delhi 110036 India National Institute of Technology Department of Computer Science and Engineering Assam Silchar788010 India

ISBN: (纸本)9798331519582

Swarm Intelligence (SI) algorithms, motivated by the group actions of natural systems, offer robust and efficient solutions to complex optimization problems. This review explores several key SI algorithms, including Cuckoo Search (CS), Firefly Algorithm (FA), Bat Algorithm (BA), and others. Each algorithm uses ideas from nature, like how ants find food and how bees interact with each other, to effectively solve nonlinear and multimodal problems. Comparative analysis focuses on convergence speed, solution quality, robustness, and computational complexity, highlighting the unique strengths and applicability of each algorithm. SI algorithms can be changed and scaled up or down easily, which makes them useful for dynamic and large-scale optimization tasks. This makes them future-proof solutions that can be used in many areas. Future research directions include new algorithms, hybrid approaches, real-world applications, and integration with machine learning techniques, promising continued advances and broader applicability. The development of SI algorithms is driven by their natural inspiration, simplicity, and potential for innovative applications, underscoring their importance in tackling diverse and complex challenges. © 2025 IEEE.

关键词： Learning algorithms

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Deep ensemble model for skin cancer classification with improved feature set

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Multimedia Tools and Applications 2025年第10期84卷 7599-7626页

作者： Burada, Sreedhar Manjunathswamy, B.E. Kumar, M. Sunil Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru560074 India Department of Computer Science and Engineering School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is (~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Enhancing real time object detection for autonomous driving using YOLO-NAS algorithm with CLEO optimizer

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International Journal of Information technology (Singapore) 2025年第3期17卷 1321-1328页

作者： Nandal, Priyanka Pahal, Sudesh Malik, Sunesh Sehrawat, Neetu Mamta Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of Electronics and Communication Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of Information Technology Maharaja Surajmal Institute of Technology New Delhi India Department of Computer Science and Engineering Panipat Institute of Engineering & Technology New Delhi India

For autonomous driving to operate in a safe and effective manner, efficient and precise object detection is essential. The efficacy of the network model is heavily challenged because of the high-speed movement of vehicles and the dynamic nature of the surrounding environment that need the detection of objects of various scales. With notable improvements over earlier iterations, the You Only Look Once-Neural Architecture Search (YOLO-NAS) algorithm is a strong contender for improving real-time object recognition in autonomous vehicles. YOLO-NAS integrated with chimpanzee leader election optimization (CLEO) has the potential to greatly enhance the safety and dependability of autonomous driving systems by utilizing its sophisticated capabilities and skill fully combining it with the vehicle’s perception system. The BDD100K dataset serves as the training and evaluation set for our suggested technique. The YOLO-NAS model with CLEO optimizer achieves mean average precision (mAP) of 83%, which is significantly higher than the mAP of 65.7% attained by the YOLO-NAS model without the optimizer. This improvement highlights the effectiveness of the CLEO optimizer in enhancing detection precision. The proposed approach was compared with existing algorithms and demonstrated remarkable performance in terms of recall, precision, and mAP. It is seen that our proposed model is better by striking equilibrium between detection speed and accuracy. Based on our findings, object detection performance for autonomous driving applications is much enhanced when utilizing our suggested approach. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Autonomous vehicles Deep learning Object detection YOLO-NAS

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Interpolated Bumpless Transfer Control for Asynchronously Switched Linear Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1579-1590页

作者： Shengao Lu Tong Wu Lixian Zhang Jianan Yang Ye Liang the School of Astronautics Harbin Institute of TechnologyHarbin 150080China the Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore IEEE

This paper revisits the problem of bumpless transfer control(BTC) for discrete-time nondeterministic switched linear systems. The general case of asynchronous switching is considered for the first time in the field of BTC for switched systems. A new approach called interpolated bumpless transfer control(IBTC) is proposed, where the bumpless transfer controllers are formulated with the combination of the two adjacent modedependent controller gains, and are interpolated for finite steps once the switching is detected. In contrast with the existing approaches, IBTC does not necessarily run through the full interval of subsystems, as well as possesses the time-varying controller gains(with more flexibility and less conservatism) achieved from a control synthesis allowing for the stability and other performance of the whole switched system. Sufficient conditions ensuring stability and H_(∞) performance of the underlying system by IBTC are developed, and numerical examples verify the theoretical findings.

关键词： Asynchronous switching bumpless transfer H_∞control switched systems

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SWIFT: Swarm Intelligence Driven ESL Synthesis for Functional Trojan Fortification 10

SWIFT: Swarm Intelligence Driven ESL Synthesis for Functiona...

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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Sengupta, Anirban Chaurasia, Rahul Indian Institute of Technology Department of Computer Science and Engineering Indore India Indian Institute of Information Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331533229

This paper presents a novel Swarm Intelligence driven Electronic System-Level (ESL) synthesis-based security methodology for functional Trojan fortification (called 'SWIFT'). Backdoor functional Trojan covertly inserted in the reusable hardware designs is a major concern for ensuring reliability functionality of consumer electronics (CE) systems and also for ensuring consumer trust. Erroneous computed output due to malicious functional Trojan can cause malfunctioning of such reusable IP cores. Therefore, the proposed methodology introduces a framework that integrates triple modular redundancy (TMR) along with a unique multi-vendor allocation that enables the generation of Trojan fortified (resistant) reusable hardware intellectual property (IP) designs capable of countering functional Trojan, if present and producing the correct output. The proposed approach integrates the swarm intelligence-based design space exploration (DSE) process with Trojan fortification design process to optimize any possible design overhead incurred due to TMR design. Results of the experimental evaluation prove that the proposed approach can generate low-cost Trojan fortified IP designs at meagre exploration time and at the expense of nominal overhead compared to prior work (that handle Trojan detection only). Further, the presented approach can handle a several potential vulnerabilities in the DSP design, which shows its robust security strength. © 2024 IEEE.

关键词： Swarm intelligence

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