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COMPARATIVE PERFORMANCE ANALYSIS OF FCFS, JOHNSON SEQUENCING AND DYNAMIC MAX-MIN JOHNSON SEQUENCING (DMMJS) ALGORITHM USING THREE SERVERS IN CLOUD computing ENVIRONMENT

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science 2023年第3期85卷 181-194页

作者： Banerjee, Pallab Roy, Sharmistha Faculty of Computing and Information Technology Usha Martin University Ranchi India

This paper presents Dynamic Max-Min Johnson scheduling algorithm that offers the optimal sequence. Johnson's rule is perhaps the most classical algorithm in the scheduling field. Dynamic Max-Min Johnson scheduling algorithm gives the most excellent way to the system flow shop to reduce the makespan in polynomial time. The sequencing problem offers with determining a most useful series of acting a number of tasks via a finite number of machines in line with some pre-assigned order with a view to optimize the output. The goal is to decide on the most desirable order of performing the jobs in such a manner that the full elapsed time may be minimal. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Scheduling algorithms

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Vehicle Plate Number Localization Using Memetic Algorithms and Convolutional Neural Networks

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Computers, Materials & Continua 2023年第2期74卷 3539-3560页

作者： Gibrael Abosamra Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers(VPLN)in challenging image *** binarization of the input image is the most important and difficult step in the detection of VPLN,a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects(CCO)and hence enriches the solution space with more solution *** to the combination of the outputs of the three binarization techniques,many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate *** pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate *** any of the previous versions will give moderate results but with very low ***,a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate *** memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used ***,a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar *** image samples with a wide range of scale,orientation,and illumination conditions have been experimented with to verify the effect of the new *** results with 97.55%detection precision have been reported using the recent challenging public Chinese City Parking Dataset(CCPD)outperforming the author of the dataset by 3.05%and the state-of-the-art technique by

关键词： Genetic algorithms memetic algorithm convolutional neural network object detection adaptive binarization filters license plate detection

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An Efficient Framework for Predicting Future Retail Sales Using Ensemble DNN-BiLSTM Technique

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SN Computer Science 2024年第1期5卷 150页

作者： Babu, K. N. Surendra Kodabagi, Mallikarjun M. School of Computing and Information Technology REVA University Bengaluru India

Forecasting retail sales often requires various number of products from different stores. Existing deep or machine learning techniques fall short of producing accurate classification results because of overfitting and two-class problem that affects the performance of evaluation parameters like precision, recall, accuracy and F-measure. Hence there is a need for an efficient prediction framework that addresses the existing problems. This work proposes an efficient framework for predicting retail sales using an ensemble DNN-BiLSTM framework. We suggest creating a base forecaster pool that includes both individual and pooled forecasting techniques for developing this ensemble approach to forecasting retail sales. Instead of focusing on finding the best individual technique, we suggest finding the optimal combination of forecasts. Classification Accuracy, Precision, Recall, and F-measure performance metrics of the experiment utilizing the proposed ensemble approach DNN + BiLSTM surpass the current DNN, CNN, and LSTM classifiers by 98.3%, 98.1%, 97.8%, and 97.94%, respectively. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Ensemble DNN + BiLSTM approach Ensemble framework Machine learning Retail dataset

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A Novel Approach for Enhanced Feature Selection Over Retails Sales Data Using Ensemble Machine Learning Technique

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SN Computer Science 2024年第5期5卷 515页

作者： Babu, K. N. Surendra Kodabagi, Mallikarjun M. School of Computing and Information Technology REVA University Bengaluru India

The connection between manufacturers and retailers in the retail industry, as well as supply chain management, depend heavily on sales forecasting. The efficiency of traditional methodologies and methods for completing a task has been undermined by the exponential growth of digital data. This study suggests an enhanced feature selection for retail sales using the Citadel POS (Point of Sales) Retail dataset using ensemble machine learning techniques. In order to predict sales data and provide in-depth analysis on retail sales and assessment, a variety of machine learning techniques are used for ensemble sales data. These techniques include diversified regression like Random Forest Regression, Gradient Boosting Regression, Linear Regression, and time series LSTM Model. The information used in this study was given from 2013 to 2019 by Citadel POS, a cloud-based solution that assists retail establishments in managing transactions, inventory, customers, vendors, monitor reports, manage sales, and tender data locally. The proposed method outperformed the regression and time series LSTM models with an MAE of 5.53 and an RMSE of 0.652. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Enhanced feature selection Ensemble machine learning Regression models Retail sales analysis Time series model

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Vehicle Detection in Challenging Scenes Using CenterNet Based Approach

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Computers, Materials & Continua 2023年第2期74卷 3647-3661页

作者： Ayesha Muhammad Javed Iqbal Iftikhar Ahmad Madini OAlassafi Ahmed SAlfakeeh Ahmed Alhomoud Department of Computer Science University of Engineering and Technology Taxila47050Pakistan Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589Saudi Arabia Faculty of Computing and Information Technology Northern Border UniversityRafhaSaudi Arabia

Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System(ITS)and reduces road *** major obstacles in automatic detection of tiny vehicles are due to occlusion,environmental conditions,illumination,view angles and variation in size of *** research centers on tiny and partially occluded vehicle detection and identification in challenging scene specifically in crowed *** this paper we present comprehensive methodology of tiny vehicle detection using Deep Neural Networks(DNN)namely *** DNN disregards objects that are small in size 5 pixels and more false positives likely to happen in crowded *** there are two categories of deep learning models single-step and two-step.A single forward pass model is the one in which detection is performed directly to possible location over dense sampling,wherein two-step models incorporated by Region proposals followed by object *** in this research scrutinize one-step State of the art(SOTA)model CenteNet as proposed recently with three different feature extractor ResNet-50,HourGlass-104 and ResNet-101 one by *** train our model on challenging KITTI dataset which outperforms in comparison with SOTA single-step technique MSSD300∗which depicts performance improvement by 20.2%mAPandSMOKEby with 13.2%mAP *** of CenterNet can be justified through the huge improved *** performance of our model is evaluated on KITTI(Karlsruhe Institute of technology and Toyota Technological Institute)benchmark dataset with different backbones such as ResNet-50 gives 62.3%mAP ResNet-10182.5%mAP,last but not the least HourGlass-104 outperforms with 98.2%mAP CenterNet-HourGlass-104 achieved high mAP among above mentioned feature *** also compare our model with other SOTA techniques.

关键词： CenteNet SOTA object detection deep learning MSSD SMOKE DNN KITTI

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Managing information and Network Security using Chaotic Bio Molecular computing Technique 6

Managing Information and Network Security using Chaotic Bio ...

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6th International Conference on Intelligent computing and Control Systems, ICICCS 2022

作者： Al-Qanour, Fahd Bin Abdullah Rajeyyagari, Sivaram Shaqra University College of Computing and Information Technology Department of Computer Science Shaqra Saudi Arabia

ISBN: (纸本)9781665410359

Requirement Elicitation is a key phase in software development. The fundamental goal of security requirement elicitation is to gather appropriate security needs and policies from stakeholders or organizations. The majority of systems fail due to incorrect elicitation procedures, affecting development time and cost. Security requirement elicitation is a major activity of requirement engineering that requires the attention of developers and other stakeholders. To produce quality requirements during software development, the authors suggested a methodology for effective requirement elicitation. Many challenges surround requirement engineering. These concerns can be connected to scope, preconceptions in requirements, etc. Other difficulties include user confusion over technological specifics, leading to confusing system aims. They also don't realize that the requirements are dynamic and prone to change. To protect the privacy of medical images, the proposed image cryptosystem uses a CCM-generated chaotic key series to confuse and diffuse them. A hexadecimal pre-processing technique is used to increase the security of color images utilising a hyper chaos-based image cryptosystem. Finally, a double-layered security system for biometric photos is built employing chaos and DNA cryptography. © 2022 IEEE.

关键词： Cryptography

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MVI and Forecast Precision Upgrade of Time Series Precipitation information for Ubiquitous computing

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Informatica (Slovenia) 2023年第5期47卷 83-94页

作者： Tripathi, Ashok Kumar Gupta, P.K. Saini, Hemraj Rathee, Geetanjali Department of Computer Science and Engineering Jaypee University of Information Technology Waknaghat173234 India School of Computing DIT University Dehradun248009 India Department of Computer Science and Engineering Netaji Subhash University New Delhi India

Missing values are the major problem in a time - series dataset that led to a very typical phenomenon in a dataset analysis. Better analysis of a dataset in ubiquitous computing can only be achieved after efficiently handling the missing values. This paper envisioned to present the missing values imputation (MVI) in the monthly rainfall dataset in India. Generally, the approximation of the missing values was completed using Non-linear principal component analysis, which is having a scope of enhancement. Kalman filter using Arima model to impute missing values is a better way that can be further improved by using Extended Kalman filtering. Further, a rainfall prediction is carried out using LSTM along with three different optimizers including stochastic gradient descent (SGD), RSM-Prop, and ADAM optimizers. In addition, a comparative prediction of rainfall is depicted which shows the combination of Extended Kalman imputation, LSTM, and ADAM optimizer to outperformance in prediction. Extended Kalman Filter (EKF), which was the primarily industrialized for the Apollo Mission. The EKF is built for the predication that is the linearization of the *** this research proposes for enhanced extended kalman filter for missing values imputation. The outcomes display that the proposed EKF with the data imputation is able to precisely estimation the rain fall compactness even when data is not available, and it help for weather prediction. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Time series

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IoT-CDS:Internet of Things Cyberattack Detecting System Based on Deep Learning Models

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Computers, Materials & Continua 2024年第12期81卷 4265-4283页

作者： Monir Abdullah Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

The rapid growth and pervasive presence of the Internet of Things(IoT)have led to an unparalleled increase in IoT devices,thereby intensifying worries over IoT *** learning(DL)-based intrusion detection(ID)has emerged as a vital method for protecting IoT *** rectify the deficiencies of current detection methodologies,we proposed and developed an IoT cyberattacks detection system(IoT-CDS)based on DL models for detecting bot attacks in IoT *** DL models—long short-term memory(LSTM),gated recurrent units(GRUs),and convolutional neural network-LSTM(CNN-LSTM)were suggested to detect and classify IoT *** BoT-IoT dataset was used to examine the proposed IoT-CDS system,and the dataset includes six attacks with normal *** experiments conducted on the BoT-IoT network dataset reveal that the LSTM model attained an impressive accuracy rate of 99.99%.Compared with other internal and external methods using the same dataset,it is observed that the LSTM model achieved higher accuracy *** are more efficient than GRUs and CNN-LSTMs in real-time performance and resource efficiency for cyberattack *** method,without feature selection,demonstrates advantages in training time and detection ***,the proposed approach can be extended to improve the security of various IoT applications,representing a significant contribution to IoT security.

关键词： Cyberattacks intrusion detection system deep learning internet of things

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Knowledge Discovery of the Internet of Things (IoT) Using Large Language Model

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 993-998页

作者： Alsulami, Bassma Saleh Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Internet of Things (IoT) technology quickly transformed traditional management and engagement techniques in several sectors. This work explores the trends and applications of the Internet of Things in industries, including agriculture, education, transportation, water management, air quality monitoring, underground mining, smart retail, smart home systems, and weather forecasting. The methodology involves a comprehensive review of the literature, followed by data extraction and analysis using BERT to identify key insights and patterns in IoT applications. The findings show that IoT significantly impacts the improvement of real-time monitoring, increasing efficiency, and encouraging innovative solutions in various sectors. Despite its transformative potential, cybersecurity threats, data privacy concerns, and the need for strong policy frameworks persist. The study emphasizes the necessity of multidisciplinary approaches to address these difficulties and optimize IoT implementation. Future research should focus on establishing secure IoT systems, maintaining data integrity, and encouraging collaboration between disciplines to realise the benefits of IoT technology. © (2025), (Science and information Organization). All rights reserved.

关键词： Data integrity

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Enhancing Network Design through Statistical Evaluation of MANET Routing Protocols

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Computers, Materials & Continua 2024年第7期80卷 319-339页

作者： Ibrahim Alameri Tawfik Al-Hadhrami Anjum Nazir Abdulsamad Ebrahim Yahya Atef Gharbi Faculty of Medical Sciences Jabir ibn Hayyan Medical UniversityAlkufa QuNajaf54001Iraq Computer Science Department School of Science and TechnologyNottingham Trent UniversityNottinghamNG118NSUK Department of Computing Faculty of Engineering Sciences&Technology(FEST)Hamdard UniversityKarachi74600Pakistan Department of Information Technology College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia Department of Information System College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia

This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR),and Zone Routing Protocol(ZRP).In this paper,the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis,Mann-Whitney,and *** articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility *** study is premised upon the Quality of Service(QoS)metrics of throughput,packet delivery ratio,and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network *** findings explained significant differences in the performance of different routing protocols;as a result,decisions for the selection and optimization of routing protocols can be taken effectively according to different network *** paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures.

关键词： Routing protocols statistical approach friedman MANET

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