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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Sheela, S. Latha, A.P. Jyothi, S. Vidyarani, H.J. Komala, M. Sharmila, N. Mallikarjunaswamy, S. Pavithra, G.S. Global Academy of Technology Department of Computer Science and Engineering Bengaluru India Dayananda Sagar College of Engineering Department of Information Science and Engineering Bengaluru India Dr. Ambedkar Institute of Technology Department of Information Science and Engineering Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Rns Insitute of Technology Dept of Computer Science and Alml Bengaluru India

ISBN: (纸本)9798350388602

Stockpile management is essential for industries to efficiently meet customer demands while minimizing costs. Traditional methods like the Economic Order Quantity (EOQ) model and ARIMA often struggle with adaptability and accuracy, leading to inefficiencies such as overstocking or stockouts. This study proposes using Long Short-Term Memory (LSTM) networks to improve demand forecasting in stockpile management. LSTMs are designed to handle sequential data, capturing complex patterns and long-term dependencies that conventional methods miss. The LSTM-based approach achieved up to a 0.30% improvement in forecasting accuracy, resulting in more precise inventory control and better alignment with actual demand. The proposed system, termed Deep Inventory Management (DIM), incorporates LSTM models that adapt to changes in demand by considering factors such as seasonality and trends. This adaptive approach allows for proactive inventory predictions, enhancing decision-making in procurement and stock levels. Key abbreviations include LSTM (Long Short-Term Memory), EOQ (Economic Order Quantity), and DIM (Deep Inventory Management). By aligning inventory more closely with demand, this deep learning approach enhances operational efficiency, reduces costs, and improves customer satisfaction, making it a valuable advancement over traditional inventory management methods. © 2024 IEEE.

关键词： Semantic Segmentation

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Synchronization and implementation of real-time traffic signal optimization regulator

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Multimedia Tools and Applications 2025年第12期84卷 10725-10745页

作者： Varshney, Abhilasha Dakshayini, M. Gururaj, H.L. Hu, Yu-Chen Department of Information Science and Engineering B.M.S. College of Engineering Karnataka Bangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung407224 Taiwan

In most cosmopolitan cities like Bengaluru, the major problems are the woes of congestion, such as increased vehicular density, travel time, stress, and vehicular carbon emissions are the major problems. These issues have led to quantifiable and non-quantifiable losses, such as high vehicle operation and maintenance costs, reduced human productivity and mobility, increased health hazards, etc. Sustainable transportation coupled with Technological interventions could alleviate these issues by efficiently managing the traffic flow. The major aims of this work are to reduce the overall commute time, traffic congestion, and air pollution, making the commutation of public stress-free and happy. So, we propose a comprehensive Real-time Traffic Signal Optimizer and Regulator (RTSOR) that identifies the right High-Density Corridors (HDC) and achieves these aims by successfully attaining the coordination among the Adaptive Traffic Signal Control System (ATSCS) fixed on the successive junctions of HDC considered in this work as a pilot initiative by upgrading the existing traffic signal in Bengaluru City to the latest ATSCS technology. Implementing this proposed RTSOR has effectively lessened the waiting time in traffic, travel time, and fuel consumption and improved air quality by reducing pollution and lowering stress levels, leading to improved quality of health and life of the public. In Bengaluru city, the current innovation would be implemented for the first time, and no prior model exists that could help optimize the travel time. The method that the study envisaged through RTSOR focuses on collecting real-time data on an HDC to elucidate our claim of originality in reducing the delay time on roads by signal synchronization. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Travel time

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A Traffic-Aware Task Mapping Problem in Fog-Cloud of Things

A Traffic-Aware Task Mapping Problem in Fog-Cloud of Things

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Srichandan, Suresh Kumar Jena, Sudarson Majhi, Santosh Kumar Mishra, Kaushik Veer Suredra Sai University of Technology Department of Computer Science and Engineering Odisha Sambalpur Burla India Sambalpur University Institute of Information Technology Sambalpur Univesity Department of Computer Science and Engineering Odisha India Guru Ghasidash Vishwavidyalaya Department of Computer Science and Information Technology C. G. Birlaspur India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350379525

The Internet of Things (loT) is rapidly advancing, but its limited resources are often supplemented by integrating fog computing to address these constraints. Despite this, fog devices encounter challenges such as heterogeneity, widespread distribution, dynamic nature, and resource limitations. To tackle these issues, a robust task scheduling strategy is crucial for efficiently utilizing fog resources and enhancing Quality of Service (QoS). Moreover, tasks generated are dynamic in nature with disparate requirements such as deadline, cost, priority, etc. Fog resources could be utilized effectively with proper mapping of tasks based on ingress traffic on each node to meet the requirements. This research formulates the task mapping problem (TMP) with the goal of minimizing cost while improving QoS by reducing response times and deadline violations for loT tasks. It uses SVM (Support Vector Machine) to predict fog node traffic and categorizes nodes into peak-traffic and low-traffic groups. The Quantum-inspired PSO-based TM Algorithm (QPSO) has been implemented to map the tasks on these nodes. Simulation results reveal that the proposed method offers significant improvements, achieving up to 19.66% better response times, 30% cost reduction, and a 26% deadline violation rate i.e., an increase in meeting task deadlines compared to other approaches. © 2024 IEEE.

关键词： Cost reduction

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An Adaptive Reversible Data Hiding Scheme using Image Processing and Deep Learning Techniques 5

An Adaptive Reversible Data Hiding Scheme using Image Proces...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Allwyn, Philip Hemalatha, J. Sampathkumar, Nithyanantham Ramaraj, Kottaimalai Swain, Gandharba Sekar, M. AAA College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Sivakasi India AAA College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Sivakasi India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhrapradesh Vaddeswaram India AAA College of Engineering and Technology Department of Mechanical Engineering Tamilnadu Sivakasi India

ISBN: (纸本)9798331530013

Image steganography is the process of hiding data from digital photos without changing how they look on the outside. It functions as a covert mode of communication, giving access to confidential data without raising the suspicions of opponents or unauthorized parties. It provides an extra degree of secrecy and security for digital communications. Prediction-error expansion (PEE) is an extensively used method in the type of reversible data hiding (RDH) that hiding data with intact image quality in the prediction errors. High-accuracy prediction algorithms are typically essential for a PEE-based RDH implementation to be successful. Current PEE-based Reversible Data Hiding (RDH) systems frequently use a single forecasting approach, typically with worldwide optimization, but pay little attention to the local neighborhoods' pixel distribution features. This study aims to analyze the most recent RDH techniques and investigate image processing and deep learning (DL) techniques for reversible data hiding (DH) schemes. RDH is a difficult topic in general which has potential uses in today's digital world. Reversible data hiding techniques properly restore the cover medium while safely transferring secret data (SD). The goal of RDH techniques is to achieve both large capacity and tunable quality. Even though reversible data hiding has been the subject of indepth research recently, there hasn't yet been a thorough literature analysis to identify research gaps and potential future approaches. © 2024 IEEE.

关键词： Data Hiding Data Security Image Processing Pixel Sorting Reversible Data Hiding Steganography

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A Segmentation-Based Scheme to Expedite Block Propagation in Blockchain Networks 21

A Segmentation-Based Scheme to Expedite Block Propagation in...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Qiu, Zhihan Zhao, Qinglin Feng, Li Zhou, MengChu Shen, Meng Liu, Yi Sun, Yi School of Computer Science and Engineering Macau University of Science and Technology Taipa China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China School of Computer Science Guangdong University of Technology Guangzhou510006 China Institute of Computing Technology Chinese Academy of Sciences Beijing100045 China

ISBN: (纸本)9798350365221

Blockchain technology, the foundation of cryptocurrencies like Bitcoin, has utility beyond finance due to its decentralized and secure transactional nature. However, today's blockchain networks face the challenge of low transaction throughput due to block propagation delays. In this study, we propose a novel approach that divides large blocks into segments and leverages multi-origin pipelining for segment dissemination, thereby expediting block propagation. The scheme also integrates the use of a Bloom filter for lightweight segment integrity verification to prevent the reception of tampered segments. Simulation results reveal that our scheme outperforms Bitcoin's legacy approach, demonstrating significant improvement in propagation speed, particularly for large blocks, while maintaining a low fork rate. © 2024 IEEE.

关键词： Blockchain

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AI Enhanced Phishing Detection System 3

AI Enhanced Phishing Detection System

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Chinnasamy, P. Krishnamoorthy, P. Alankruthi, K. Mohanraj, T. Kumar, B. Santhosh Chandran, Likha Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Thiruthangal Nadar College Pg Department of Computer Science Chennai51 India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Ibrahimpatnam501506 India Mlr Institute of Technology Department of Science and Humanities Hyderabad India

ISBN: (纸本)9798350361186

It attempts to create a sophisticated system that uses artificial intelligence (AI) methods to prevent and detect phishing assaults in advance. Cybersecurity is seriously threatened by phishing attempts, which take advantage of human weakness to trick people into disclosing private information. In order to precisely identify and categorize any phishing attempts, the suggested method makes use of machine learning algorithms and natural language processing (NLP) to examine a variety of characteristics of emails, websites, and digital *** project uses AI-driven methods to improve cybersecurity defenses already in place by offering a more reliable and effective way to detect and stop phishing assaults, protecting people and businesses from the negative effects of cyberattacks. © 2024 IEEE.

关键词： Denial-of-service attack

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A Review on Binary Code Analysis Datasets 18th

A Review on Binary Code Analysis Datasets

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Huang, Zhijian Song, Shuguang Liu, Han Kuang, Hongyu Zhang, Jingjing Hu, Pengfei National Key Laboratory of Science and Technology on Information System Security Systems Engineering Institute Academy of Military Sciences Beijing China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031714696

Binary code analysis serves as the foundation for research in vulnerability discovery, software protection, and malicious code analysis. However, analyzing binary files is challenging due to the lack of high-level semantic information, leading to heavy dependence on analysts’ expertise and significantly impacting the efficiency of binary code analysis. Recent years has witnessed the blossom of machine learning models for binary analysis, but few researches address the problem of binary code datasets. In this paper, we review all the existing and available datasets, and make classification according to their application. We set up experiments to illustrate how dataset quality could affect the performance of machine learning models in binary function recognition. Based on the experimental evaluation, we present a discussion on the ground truth as well as quality evaluation problems for binary code datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Synergizing Coati Optimization and Hybrid Neural Architecture for Optimized Psychological Disease Prediction 2

Synergizing Coati Optimization and Hybrid Neural Architectur...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Mekala, M. Reshma, N. Chinnaraj, Aparna Karthika, R. Sundareswari, K. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Rathinam Technical Campus Department of Information Technology Coimbatore India Rathinam Technical Campus Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331534950

Predicting psychological diseases using machine learning has gained prominence in healthcare informatics. This research introduces an Optimized Enhanced Hybrid Approach (OEHA) by integrating the Coati Optimization Algorithm with the previously proposed Hybrid Hopfield Recurrent Neural Network (H2RN2) model. This synergy aims to optimize feature selection in psychological disease prediction, ultimately leading to improved accuracy and efficiency. This research also proposes an enhanced hybrid approach that combines the power of Convolutional Long Short-Term Memory (ConvLSTM) with hybrid hopfield attention layer to predict psychological diseases through analysis of clinical Electronic Health Records (EHRs). This approach can handle the multimodal data by using ConvLSTM while addressing the challenges LSTM alone might struggle to effectively capture spatial information in clinical data. By using attention layer with hopfield network, variable-length sequences are handled, parallel processing is achieved, vanishing gradient problem is vanished, leading to accurate prediction. © 2024 IEEE.

关键词： Electronic health record

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Vehicle Theft Detection Using Neural Pattern Recognition 3

Vehicle Theft Detection Using Neural Pattern Recognition

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Jane Rubel Angelina, J. Subhashini, S.J. Kumar Rapuri, Anil Ravva, Vennela Rasala, Raghavendra Ray Adurgam, Sai Dcepak Kalasalingam Academy of Research and Education Dept of Computer Science and Engineering TamilNadu Krishnankoil India SRM Madurai College for Engineering and Technology Dept of Computer Science and Engineering TamilNadu Sivagangai India

ISBN: (纸本)9798350322842

Finding stolen cars is becoming increasingly important in many urban regions. An automated system for scanning license plates can recognize vehicle numbers without the need for human interaction. This work proposes a vehicle theft detection system based on neural pattern recognition, gaussian filter, and equilibrium optimization. The proposed architecture has significant speedup and higher accuracy rates. The proposed number plate recognition method has a maximum accuracy rate of 94% and an average reduction in the processing time of 32%. Pattern recognition is the process of detecting regularities and patterns in data using machine learning. These analogies may now be uncovered via statistical analysis, historical data, or machine-generated knowledge. © 2023 IEEE.

关键词： Image segmentation

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Multi-granularity feature enhancement network for maritime ship detection

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CAAI Transactions on Intelligence technology 2024年第3期9卷 649-664页

作者： Li Ying Duoqian Miao Zhifei Zhang Hongyun Zhang Witold Pedrycz Department of Computer Science and Technology Tongji UniversityShanghaiChina Project Management Office of China National Scientific Seafloor Observatory Tongji UniversityShanghaiChina Department of Electrical and Computer Engineering AlbertaEdmontonCanada System Research Institute Polish Academy of SciencesWarsawPoland

Due to the characteristics of high resolution and rich texture information,visible light images are widely used for maritime ship ***,these images are suscep-tible to sea fog and ships of different sizes,which can result in missed detections and false alarms,ultimately resulting in lower detection *** address these issues,a novel multi-granularity feature enhancement network,MFENet,which includes a three-way dehazing module(3WDM)and a multi-granularity feature enhancement module(MFEM)is *** 3WDM eliminates sea fog interference by using an image clarity automatic classification algorithm based on three-way decisions and FFA-Net to obtain clear image ***,the MFEM improves the accuracy of detecting ships of different sizes by utilising an improved super-resolution reconstruction con-volutional neural network to enhance the resolution and semantic representation capa-bility of the feature maps from *** results demonstrate that MFENet surpasses the other 15 competing models in terms of the mean Average Pre-cision metric on two benchmark datasets,achieving 96.28%on the McShips dataset and 97.71%on the SeaShips dataset.

关键词： object classification object recognition rough sets rough set theory

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