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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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WOCP: Controller Placement using Whale Optimization in SDN-WAN 4

WOCP: Controller Placement using Whale Optimization in SDN-W...

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

作者： Thalapala, Vidya Sagar Guravaiah, Koppala Department of Computer Science & Engineering Indian Institute of Information Technology Kerala Kottayam686635 India

The established SDN multi-controller architecture faces the challenge of locating controllers to boost the scalability, reliability, and security of the network. Reliability is one of the important metric to handle when a failure event occurs in a network. In this paper, a Whale Optimization technique is used for controller placement and proposed Whale Optimization based Controller Placement (WOCP), that places the controllers in an optimized and efficient manner. The adequate placement of controllers minimizes the overall average latency with a high reliability index. The WOCP method resemblances with Minimizing Latency for Controller Placement (MLCP), resulting in an 18% reduction in latency in the Geant network topology. © 2025 The Authors. Published by Elsevier B.V.

关键词： Software reliability

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SIGN LANGUAGE RECOGNITION USING CNN 2

SIGN LANGUAGE RECOGNITION USING CNN

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Allam, Sangeetha Balaram, Allam Rao, Yedavalli Venkata Raghava Kiran, Ajmeera Valpadasu, Hema Navya, Soleti Mlr Institute of Technology Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Cse Aiml Avniet Jntuh Aiml Dept India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad Dundigal India Computer Science and Engineering Mlr Institute of Technology Telangana Hyderabad India Mlr Institute of Technology Department of Computer Science and Information Technology India

ISBN: (纸本)9798350370188

Conversation between both the deaf and the general population is becoming exceedingly challenging, and there is no reputable translator accessible in society to aid. This program enables real-time voice and signal language translation, in distinctive: 1. recognizing male or female signals 2. Establishing a system learning model for interpreting image-to-text data. 3. Seeking innovative terms Four. Constructing Sentences 5. After creating all of the content, 6. Is to obtain the audio output. The goal of this research is to develop a framework for a potential sign language interpreter who could also translate conversations between sign languages into written and spoken English. If there had a translator like this, it would be much easier for many deaf and partially deaf people to interact with others in everyday situations © 2024 IEEE.

关键词： Translation (languages)

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Development of an Ontology-Based Technique for Labeling Land Cover Classes with Minimum Utilization of SAR Features

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SN computer science 2023年第6期4卷 731页

作者： Gupta, Shruti Singh, Dharmendra Kumar, Sandeep Computer Science and Engineering and Information Technology Department Jaypee Institute of Information Technology Noida India Electronics and Communication Engineering Department Indian Institute of Technology Roorkee Roorkee India Computer Science and Engineering Department Indian Institute of Technology Roorkee Roorkee India

The availability of satellite imagery to "domain" experts, along with advancements in image processing and analysis techniques, has revolutionized numerous fields, enabling better understanding, planning, and management of our planet's resources. However, formalizing the knowledge gained from domain experts is essential for preserving, sharing, and leveraging their expertise. In this context, knowledge representation methods like ontology play a significant role in the development of applications based on satellite image analysis. Land cover labeling is one of the significant applications of satellite image analysis and plays a vital role in various domains by providing valuable information about the Earth's surface. Although several works have been reported to focus on land cover land use classification and labeling, very few are properly documented or formalized. Therefore, in this paper, an LCL (land cover labeling) ontology has been proposed for labeling the land cover classes using PALSAR-I satellite data. The ontology is based on an adaptive algorithm, as the labeling criterion is independent of specific range values and depends on feature image statistics. For algorithm development, four types of features, namely, polarimetric features, texture features, color features, and wavelet features were examined. For selecting optimal feature set, random forest was utilized and, consequently, for further labeling the classes a set of rules has been formed by applying Otsu thresholding on the selected class-wise feature sets. The derived rules were finally formalized to develop the ontology for labeling the land cover classes. The proposed ontology was applied to distinct study sites using PALSAR-I data which resulted in a satisfactory classification accuracy of around 85%. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Color features Ontology PALSAR Polarimetric features Random forests Texture features Wavelet features

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Inf-TESLA++: A Blockchain-assisted Continuous Authentication Protocol for Broadcast Communication in Resource-Constrained Devices 18

Inf-TESLA++: A Blockchain-assisted Continuous Authentication...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Dorsala, Mallikarjun Reddy Nishitha, Charugundla Lakshmi Sathwik, Pendem Srilekha, Gattu Morampudi, Mahesh Kumar Indian Institute of Information Technology Sri City Department of Computer Science and Engineering Chittoor India Department of Computer Science and Engineering AP India

ISBN: (纸本)9798350391725

Many applications widely use broadcast communications (BC) due to their efficiency in simultaneously distributing data to many receivers. More specifically, BC is essential in resource-constrained devices (RCDs) for conserving energy and bandwidth, which are vital for increasing the longevity and performance of RCDs. The three major challenges in BC are source authentication, data integrity and continuous authentication (CA). The first two challenges are effectively addressed in TESLA and its variants. However, achieving CA without incurring overheads on the sender and receiver sides remains an unresolved challenge. In this paper, we propose Infinite (Inf) TESLA++, assisted by Blockchain and smart contracts, to address the CA challenge with minimal overhead at sender and receiver. Our theoretical and simulation analysis demonstrates that Inf-TESLA++ incurs minimal overhead compared to existing protocols while eliminating the need for direct communication between the sender and receiver for synchronization. © 2024 IEEE.

关键词： Authentication Protocol

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Enhancing User Experience and Integrating Passwordless FIDO-Based Authentication through Bluetooth Low Energy (BLE) 3

Enhancing User Experience and Integrating Passwordless FIDO-...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Rathi, Snehal Oswal, Harsh D'Souza, Ansfred Pratham, Shaantanu Khedkar, Vijayshri BRACTs Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Department of Computer Science & Engineering Pune India

ISBN: (纸本)9798350354348

Traditional password-based authentication methods confront several issues such as security weaknesses, user inconvenience, and susceptibility to various attacks, e.g. phishing. These issues are solved with newly created industry standards such as Web Authentication, CTAP (Client to Authenticator Protocol), FIDO (Fast Identity Online), and W3C guidelines that deliver reliable and interoperable authentication solutions. This research explores how the FIDO, Web Authentication, CTAP, and W3C standards can be blended to revolutionize authentication practices. The proposed method should increase the security and usability of the authentication process without requiring passwords. Our technology facilitates this by establishing secure connections over Bluetooth, offering seamless UX - between a mobile device (in your pocket) and single desktop while maintaining the strongest security postures through structured authentication experiences. © 2024 IEEE.

关键词： Authentication Protocol

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A Bilateral Assessment of Human Activities Using PSO-Based Feature Optimization and Non-linear Multi-task Least Squares Twin Support Vector Machine

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SN computer science 2024年第3期5卷 315页

作者： Thakur, Ujwala Vidyarthi, Ankit Prajapati, Amarjeet Department of Computer Science Engineering and Information Technology Jaypee Institute of Information Technology Noida Sector-62 Uttar Pradesh Noida 201309 India

Human activity recognition (HAR) is an essential part of many applications, including smart surroundings, sports analysis, and healthcare. Accurately categorizing intricate actions from sensor data is still difficult, though. This method infers an individual’s actions using a variety of sensor data, including magnetometers, gyroscopes, and accelerometers. This work suggests a unique method for classifying human activities by combining a non-linear multi-task least squares twin support vector machine (NMtLSSVM) with particle swarm optimization (PSO) for feature optimization. Utilizing the advantages of both approaches, the suggested strategy achieves excellent resilience and accuracy in activity identification. The suggested method reduces dimensionality and computing costs while maintaining pertinent information using PSO to extract the most important features from the raw sensor data. On the other hand, NMtLSSVM is used to construct a multi-task learning framework that learns several related tasks at the same time and shares information among them. As a result, generalization and resilience can be enhanced beyond single-task models. Two datasets that are available to the public, WISDM, and UCI-HAR, were used to assess the suggested method. The testing findings show that the suggested approach works noticeably better than the most advanced techniques already in use. Overall activities, the outcome achieves an average classification accuracy of 97.8% (UCI-HAR dataset) and 98.5% (WISDM dataset). Furthermore, the PSO-based feature optimization decreased the feature count by 60% without sacrificing efficiency. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Human action recognition Intelligent computing Machine learning classification Performance comparison Sensory data analysis

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A Computational Model for Driver Risk Evaluation and Crash Prediction Using Contextual Data from On-board Telematics

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Recent Advances in computer science and Communications 2022年第4期15卷 620-635页

作者： Jain, Neetika Mittal, Sangeeta Department of Computer Science & Engineering and Information Technology Faculty of Engineering Jaypee Institute of Information Technology Noida India

Introduction: Vehicle crashes can be hazardous to public safety and may cause infrastructure damage. Risky driving significantly raises the possibility of the occurrence of a vehicle crash. As per statistics by the World Health Organization (WHO), approximately 1.35 million people are involved in road traffic crashes resulting in loss of life or physical disability. WHO attributes events like over-speeding, drunken driving, distracted driving, dilapidated road infrastructure and unsafe practices such as non-use of helmets and seatbelts to road traffic accidents. As these driving events negatively affect driving quality and enhance the risk of a vehicle crash, they are termed as negative driving attributes. Methods: A multi-level hierarchical fuzzy rules-based computational model has been designed to capture risky driving by a driver as a driving risk index. Data from the onboard telematics device and vehicle controller area network is used for capturing the required information in a naturalistic way during actual driving conditions. Fuzzy rules-based aggregation and inference mechanisms have been designed to alert about the possibility of a crash due to the onset of risky driving. Results: On-board telematics data of 3213 sub-trips of 19 drivers has been utilized to learn long term risky driving attributes. Furthermore, the current trip assessment of these drivers demonstrates the efficacy of the proposed model in correctly modeling the driving risk index of all of them, including 7 drivers who were involved in a crash after the monitored trip. Conclusion: In this work, risky driving behavior has been associated not just with rash driving but also other contextual data like driver’s long-term risk aptitude and environmental context such as type of roads, traffic volume and weather conditions. Trip-wise risky driving behavior of six out of seven drivers, who had met with a crash during that trip, was correctly predicted during evaluation. Similarly, for the other 12

关键词： Telematics

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Enhanced Agricultural Decision-Making: Merging Climatic Data with Machine Learning Algorithms 5

Enhanced Agricultural Decision-Making: Merging Climatic Data...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Rathi, Snehal Wamanacharya, Shubham Shah, Samkit Khedkar, Vijayshri BTACT'S Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Symbiosis Institute of Technology Department of Computer Science & Engineering Pune India

ISBN: (纸本)9798350389609

Climate change still implies risks for economic security, such as for agriculture and its productivity and, therefore, national economic potential. Although similar approaches have been used in publications concerning individual crops or soil types, the application of climatology parameters in agriculture and it limited indices have not been optimal. Owing to the recognized limitations of traditional linear productivity models, this thesis subjects' agricultural productivity to a Cobb-Douglas multi-factorial modeling approach through intensive levels of data decomposition. The proposed approach enabled the researchers to adjust the impact of climate parameters during students' evaluations in the secondary education curricula. Additionally, frameworks which relate temperature variations and crop growth do exist, and therefore the temperature and agriculture performance model effects determinants were determined using complex analysis of temperature relations and crop outputs. This highlights the various factors affecting production, its structure, and the relative importance of some conditions in agriculture and explains their dynamics depending on the changes in growth factors. Hence, a set of factors employed in the modeling resulted mainly from economic conditions, production system structures and advanced agricultural technology applications in the economy as prediction © 2024 IEEE.

关键词： Agricultural Data Analysis Climate Impact on Agriculture Crop Growth Days Growth Degree Days (GDD) R Programming

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Weighted Ensemble Learning for Accident Severity Classification Using Social Media Data

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SN computer science 2024年第5期5卷 528页

作者： Raul, Sanjib Kumar Rout, Rashmi Ranjan Somayajulu, D.V.L.N. Department of Computer Science and Engineering National Institute of Technology Telangana Warangal 506004 India Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing (IIITDM) Andhra Pradesh Kurnool 518002 India

Due to the heavy increase in social media usage, like Twitter, there is a growing interest in the research community in developing automation tools like accident severity-based tweet classification models. These tools aid in automatically extracting severity information from the accident tweet content. Moreover, prediction models are essential for predicting the severity of an accident to increase the safety efficacy of the road traffic system. However, the difficulty lies in the collection of sufficient labeled data. We propose a model called weighted ensemble-based self-training with decision tree (WESTDT), a semi-supervised methodology with a dynamic data labeling strategy. The base classifier in this model is a homogeneous weighted ensemble of decision tree classifiers for better prediction of pseudo labels. We also propose a novel performance measure called risk factor to estimate the amount of risk present in the application using the prediction model. The proposed model outperformed the state-of-the-art model, namely reliable semi-supervised ensemble learning (RESSEL), and the baseline models, namely decision tree (DT) and self-training with decision tree (STDT), in terms of both the traditional and the proposed measures. The results indicate that the proposed framework outperforms all other models on all datasets in terms of precision, recall, and accuracy by a range of 5–18.3%, 3.9–9.3%, and 6.6%, respectively. These findings can be helpful for the development of efficient and sustainable systems for traffic management and safety. It is also crucial for assisting government authorities in devising prompt, proactive strategies to prevent traffic accidents and enhance road safety. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Accident severity classification Decision tree Self-training Semi-supervised learning Weighted ensemble learning

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