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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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Open-source solutions for real-time data retrieval in industrial automation and IoT environments

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International Journal of Internet Manufacturing and Services 2025年第1期11卷 73-92页

作者： Hegde, Seema B. Reddy, Tadigotla Narendra Prasannan, Prasad Manjunath, K.V. Herbert, Mervin A. Rao, Shrikantha S. Department of Computer Science and Engineering Global Academy of Technology Bengaluru560098 India Smart Manufacturing Precision Machine Tools Central Manufacturing Technology Institute Bengaluru22 India Department of Industrial Engineering and Management Siddaganga Institute of Technology Tumkur572103 India Department of Mechanical Engineering National Institute of Technology Karnataka Surathkal India

Digitalisation of the manufacturing industries due to the implementation of the ‘industrial internet of things (IIOT)’ is a key enabler for improved productivity and reliability at a reduced labour cost. The industrial IOT connects all the industrial machines such as PLCs, CNCs, and robots through a robust network. The generated data by these end devices plays a vital role in industrial automation, however acquiring the data from machines specifically legacy machines using various communication protocols is the biggest challenge and costly process, especially for MSMEs. Hence this paper discusses the usage of the open-source framework for real-time data acquisition from industrial machines and its implication in Industry 4.0. The paper implements and validates the possibility of the usage of an open-source framework for data acquisition instead of vendor-specific licensed software using several test cases. The paper also validates and proves ‘Wireshark’ can be a universal open-source solution for data acquisition using any standard communication protocols from various vendor-specific machines. Hence this work provides a novel solution for the digitalisation of the MSME manufacturing industries efficiently at the reduced maintenance cost and improve their productivity. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Cost reduction

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Leveraging Large Language Models and RAPIDS AI to Detect Fraudulent AI-Generated Marketplace Reviews

Leveraging Large Language Models and RAPIDS AI to Detect Fra...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Dudeja, Nishit Pundir, Arun Singh Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India

ISBN: (纸本)9798350350821

Consumer confidence is, in the present time, a dilemma given the steadily rising number of deceptive and inaccurate AI-generated reviews on internet marketplaces. There is an urgent need for a thorough dataset, which is nowhere to be found, to address the pervasiveness of fraudulent reviews. In order to close this gap, in this work, we propose relevant detection techniques using GPU-accelerated models from the RAPIDS AI library and new age Large Language Models like GPT-2 and BERT. By carefully constructing a unique dataset of about 54,000 reviews, we have combined human-authored reviews gleaned from web scraping with AI-generated reviews produced with the Chat-GPT 3.5 model, which supports the representative spectrum across all review polarities and offers a solid basis for precise analysis. We show significant accuracy in differentiating between reviews written by humans and those produced by AI and provide an all-inclusive solution to this new issue by using sophisticated LLMs and RAPIDS AI models. Moreover, we also compare the approaches to find the most successful one. This approach not only tackles the problem of fraudulent reviews but also establishes a new benchmark for keeping online marketplaces morally sound. © 2024 IEEE.

关键词： Marketplaces

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A Novel Approach for Lightweight Sign Language Recognition Leveraging Google Mediapipe and Deep Neural Net 1

A Novel Approach for Lightweight Sign Language Recognition L...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Siju, Ijas Selvam, Prabu Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350352931

The advent of technologies like Deep Learning has revolutionized human interaction, transcending language and disability barriers. Sign Language Recognition (SLR) systems have emerged as vital tools, facilitating seamless communication between individuals and between humans and machines through Interfaces. However, existing SLR systems pose significant challenges, including the demand for high accuracy, low latency, simplicity in development and deployment, lightweight design for edge compatibility, and efficient resource utilization during training. Addressing these challenges necessitates developing a system that balances training data accuracy, ease of training, and resource efficiency. Conventional Deep learning approaches often rely on high-dimensional image datasets for training HGR models, resulting in trade-offs between latency, accuracy, complexity, and resource requirements. To mitigate these challenges, this paper harnesses the capabilities of Google Mediapipe to extract a 21-point hand landmark vector from each training image. By leveraging this low-dimensional data, we train model that are accurate, lightweight, less complex, and exhibit low latency, making them compatible with edge devices. The hand gesture images were collected for several gestures, such as peace, okay, and stop. These images will be converted to landmark vectors using Mediapipe. These landmark points were used to train the Deep Neural Network architecture developed in Tensorflow. The model was trained with each hand gesture assigned with a common word in English and the proposed model was tested with a live webcam stream. The proposed model is developed in Python in the Google Colab IDE with the help of libraries and frameworks like OpenCV, Tensorflow and Google Mediapipe. The proposed model outperforms current state-of-the-art approaches by achieving an accuracy of 9 4. 8 8 %. © 2024 IEEE.

关键词： Convolutional neural networks

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A Construction of Object Detection Model for Acute Myeloid Leukemia

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Intelligent Automation & Soft Computing 2023年第4期36卷 543-560页

作者： K.Venkatesh S.Pasupathy S.P.Raja Department of Computer Science and Engineering Annamalai UniversityChidambaram608002India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India

The evolution of bone marrow morphology is necessary in Acute Mye-loid Leukemia(AML)*** takes an enormous number of times to ana-lyze with the standardization and inter-observer ***,we proposed a novel AML detection model using a Deep Convolutional Neural Network(D-CNN).The proposed Faster R-CNN(Faster Region-Based CNN)models are trained with Morphological *** proposed Faster R-CNN model is trained using the augmented *** overcoming the Imbalanced Data problem,data augmentation techniques are *** Faster R-CNN performance was com-pared with existing transfer learning *** results show that the Faster R-CNN performance was signiﬁcant than other *** number of images in each class is *** example,the Neutrophil(segmented)class consists of 8,486 images,and Lymphocyte(atypical)class consists of eleven *** dataset is used to train the CNN for single-cell morphology classiﬁ*** proposed work implies the high-class performance server called Nvidia Tesla V100 GPU(Graphics processing unit).

关键词： Acute myeloid leukemia(AML) convolutional neural network(CNN) and nvidia tesla v100 gpu

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Human and Machine Vision Based Indian Race Classification Using Modified-Convolutional Neural Network

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computer Systems science & engineering 2023年第3期44卷 2603-2618页

作者： Vani A.Hiremani Kishore Kumar Senapati Department of Computer Science&Engineering Birla Institute of TechnologyMesraRanchi835215JharkhandIndia

The inter-class face classification problem is more reasonable than the intra-class classification *** address this issue,we have carried out empirical research on classifying Indian people to their geographical *** work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human *** have created an Automated Human Intelligence System(AHIS)to evaluate human visual *** of AHIS response showed that face shape is a discriminative feature among the other facial *** have developed a modified convolutional neural network to characterize the human vision response to improve face classification *** proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face ***,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same *** an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.

关键词： Data collection and preparation human vision analysis machine vision canny edge approximation method color local binary patterns convolutional neural network

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Optimal trained ensemble of classification model for satellite image classification

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Multimedia Tools and Applications 2025年第7期84卷 3745-3766页

作者： Singh, Sarabjeet Kundra, Harish Kundra, Sheetal Pratima, P. Venkata Devi, M. V. Anjana Kumar, Sushil Hassan, Mariya Department of Computer Science and Engineering Chandigarh College of Engineering and Technology Chandigarh160019 India Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Ranga Reddy District Telangana Ibrahimpatnam501506 India Department of Artificial Intelligence and Data Science Guru Nanak Institute of Technology Ranga Reddy District Telangana Ibrahimpatnam501506 India Department of Computer Science and Engineering Central University of Haryana Jant-Pali Mahendergarh123031 India

Satellite image classification is the most significant remote sensing method for computerized analysis and pattern detection of satellite data. This method relies on the image's diversity structures and necessitates a thorough validation of the training data using the chosen classification algorithm. The classifying of remotely sensed images refers to the process of classifying pixels into a fixed set of distinct classes based on their data values. The pixel is categorized into that class if it complies with a specific set of requirements. A new SIC scheme is suggested in this work, where, Contrast limited adaptive histogram equalization (CLAHE) is deployed for pre-processing the image. Further, statistical features, Vegetation Indices (VI) and Improved Hybrid Vegetation Index (IHVI) are extracted from the preprocessed image. Then, we deploy 2 phase classification process for classifying the image. In the first phase, Bi-Directional Gated Recurrent Unit (BI-GRU), Convolutional Neural Network (CNN) and Deep Max Out (DMO) are used. In 2nd phase, optimized RNN is used for determining the final classification result. Also, the training of the model is done by the new Chaotic Opposition Behavior Improvised Coot Algorithm (COBI-CA) optimization via tuning the optimal weights. The COOT method has been enhanced and is now the suggested algorithm. Lastly, we conduct an analysis to compare the suggested Satellite Image Classification (SIC) model's performance to the traditional approaches in terms of statistical analysis, precision, accuracy, and error measure. In comparison to other conventional methods, the accuracy of the suggested model is 97%. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2024.

关键词： Pixels

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Energy efficient quantum-informed ant colony optimization algorithms for industrial internet of things

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1077-1086页

作者： Jannu, Srikanth Dara, Suresh Thuppari, Chaitanya Vidyarthi, Ankit Ghosh, Debjani Tiwari, Prayag Muhammad, Ghulam Department of Computer Science and Engineering Vaagdevi Engineering College Singaram506005 India School of Technology Woxsen University HyderabadTS 502 345 India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida201309 India Department of Computer Science and Engineering Bennett University Greater Noida201309 India School of Information Technology Halmstad University Halmstad301 18 Sweden Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11451 Saudi Arabia

One of themost prominent research areas in information technology is the Internet of Things (IoT) as its applications are widely used, such as structural monitoring, health care management systems, agriculture and battlefield management, and so on. Due to its self-organizing network and simple installation of the network, the researchers have been attracted to pursue research in the various fields of IoTs. However, a huge amount of work has been addressed on various problems confronted by IoT. The nodes densely deploy over critical environments and those are operated on tiny batteries. Moreover, the replacement of dead batteries in the nodes is almost impractical. Therefore, the problem of energy preservation and maximization of IoT networks has become the most prominent research area. However, numerous state-of-The-Art algorithms have addressed this issue. Thus, it has become necessary to gather the information and send it to the base station in an optimized method to maximize the network. Therefore, in this article, we propose a novel quantum-informed ant colony optimization (ACO) routing algorithm with the efficient encoding scheme of cluster head selection and derivation of information heuristic factors. The algorithm has been tested by simulation for various network scenarios. The simulation results of the proposed algorithm show its efficacy over a few existing evolutionary algorithms using various performance metrics, such as residual energy of the network, network lifetime, and the number of live IoT nodes. Impact Statement-Toward IoT-based applications, here we presented the Quantum-inspired ACO clustering algorithm for network lifetime. IoT nodes in the clustering phase choose theirCH through the distance between cluster member IoT nodes and the residual energy. Thus, CH selection reduces the energy consumption of member IoT nodes. Therefore, our significant contributions are summarized as follows. i. Developing Quantum-informed ACO clustered routing algor

关键词： Energy efficiency

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Music Generation Algorithms: An In-Depth Review of Future Directions and Applications Explored

Music Generation Algorithms: An In-Depth Review of Future Di...

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Duraisamy, Premkumar Naveen, V. Kishore, G.N. Nishal, S. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350374100

Music generation algorithms have made significant progress in recent years, enabling the development of algorithms to generate creative and realistic music. This survey paper provides a comprehensive overview of music generation algorithms organized by algorithm type. We discuss each algorithm type's strengths, weaknesses, and applications. We also cover multi-level representations for music generation, which allow algorithms to learn complex patterns in music data at different levels of granularity. This survey paper is intended to be a valuable resource for researchers and practitioners in music generation. It provides a comprehensive overview of the current state of the art in music generation algorithms and highlights this research area's challenges and future directions. © 2024 IEEE.

关键词： algorithms applications multi-level representations music generation strengths weaknesses

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Enhancing Ophthalmic Diagnostics: A New Horizon for OCT Retinal Analysis

Enhancing Ophthalmic Diagnostics: A New Horizon for OCT Reti...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Priyanka, S. Selvam, Prabu Vellore Institute of Technology School of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350361537

Ophthalmic diagnostics play a critical role in the early detection and management of various ocular diseases. Among the advanced imaging modalities employed in ophthalmology, Optical Coherence Tomography (OCT) has emerged as a cornerstone technology for non-invasive, high-resolution imaging of ocular structures. This paper analyzes the efficiency of three deep learning models such as VGG16, ResNet50, and InceptionV3, in Optical Coherence Tomography (OCT) images. This study compares the efficiency of these deep learning models in classifying four different pathological retinal conditions: Normal Retina (NR), Choroidal NeoVascularization (CNV), Diabetic Macular Edema (DME), and Early Age-Related Macular Degeneration (EAMD). In addition, we have performed two preprocessing operations, image enhancement and data augmentation, to improve the image quality and to over the data imbalance, respectively. We assessed the performance of these deep learning models using a benchmark dataset. After the experiments, we found that the VGG16 outperforms ResNet50 and InceptionV3 by accomplishing an accuracy of 98.7%. © 2024 IEEE.

关键词： Optical coherence tomography

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