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3-D Packing in Container using Teaching Learning Based Optimization Algorithm
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The paper aims to propose Teaching Learning based Optimization (TLBO) algorithm to solve 3-D packing problem in containers. The objective which can be presented in a mathematical model is optimizing the space usage in a container. Besides the interaction effect between students and teacher, this algorithm also observes the learning process between students in the classroom which does not need any control parameters. Thus, TLBO provides the teachers phase and students phase as its main updating process to find the best solution. More precisely, to validate the algorithm effectiveness, it was implemented in three sample cases. There was small data which had 5 size-types of items with 12 units, medium data which had 10 size-types of items with 106 units, and large data which had 20 size-types of items with 110 units. Moreover, it was also compared with another algorithm called Gravitational Search Algorithm (GSA). According to the computational results in those example cases, it can be concluded that higher number of population and iterations can bring higher chances to obtain a better solution. Finally, TLBO shows better performance in solving the 3-D packing problem compared with GSA.          

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Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Development of an Optimized Botnet Detection Framework based on Filters of Features and Machine Learning Classifiers using CICIDS2017 Dataset
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Abstract<p>Botnet is a malicious activity that tries to disrupt traffic of service in a server or network and causes great harm to the network. In modern years, Botnets became one of the threads that constantly evolving. IDS (intrusion detection system) is one type of solutions used to detect anomalies of networks and played an increasing role in the computer security and information systems. It follows different events in computer to decide to occur an intrusion or not, and it used to build a strategic decision for security purposes. The current paper <italic>suggests</italic> a hybrid detection Botnet model using machine learning approach, performed and analyzed to detect Botnet atta</p> ... Show More
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Publication Date
Fri Feb 17 2023
Journal Name
Sustainability
Sustainable Utilization of Machine-Vision-Technique-Based Algorithm in Objective Evaluation of Confocal Microscope Images
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Confocal microscope imaging has become popular in biotechnology labs. Confocal imaging technology utilizes fluorescence optics, where laser light is focused onto a specific spot at a defined depth in the sample. A considerable number of images are produced regularly during the process of research. These images require methods of unbiased quantification to have meaningful analyses. Increasing efforts to tie reimbursement to outcomes will likely increase the need for objective data in analyzing confocal microscope images in the coming years. Utilizing visual quantification methods to quantify confocal images with naked human eyes is an essential but often underreported outcome measure due to the time required for manual counting and e

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Publication Date
Sat Aug 25 2012
Journal Name
Wireless Personal Communications
Multi-Objective Evolutionary Algorithm Based on Decomposition for Energy Efficient Coverage in Wireless Sensor Networks
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Publication Date
Sun Sep 03 2023
Journal Name
Iraqi Journal Of Computers, Communications, Control & Systems Engineering (ijccce)
Efficient Iris Image Recognition System Based on Machine Learning Approach
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HM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023

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Publication Date
Sat Nov 02 2019
Journal Name
Advances In Intelligent Systems And Computing
Modified Opposition Based Learning to Improve Harmony Search Variants Exploration
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Publication Date
Thu Jun 30 2011
Journal Name
Al-khwarizmi Engineering Journal
Performance Improvement of Neural Network Based RLS Channel Estimators in MIMO-OFDM Systems
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The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust a

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Publication Date
Wed May 10 2017
Journal Name
Journal Of The College Of Languages (jcl)
PROBLÉMATIQUE DE L’ENSEIGNEMENT DE LA TRADUCTION ET PROLÈMES DE SON APPRENTISSAGE DANS UNE CLASSE DE LANGUE The problem in teaching Translation and problems of its learning in a language lesson
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Le présent travail aborde la question de l’enseignement de traduction en tant que matière faisant partie du programme élaboré dans des Départements de Français au sein de certaines universités irakiennes, en particulier celle de Bagdad. La méthode d’enseigner suivie constitue une véritable problématique qu’on a bien diagnostiquée à partir de quelques années d’expériences, à la lumière des observations faites dans des cours de traduction professionnelle, et dans la perspective des citations et témoignages établies par des traductologues et pédagogues et principalement par Marianne LEDERER qui a établi la Théorie Interprétative de la traduction. Mais pourquoi l’enseignement lui-même poserait une telle probl

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Publication Date
Tue Jun 14 2022
Journal Name
Chemical Papers
Nile red based dye D–π–A as a promising material for solar cell applications
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A recently reported Nile red (NR) dye conjugated with benzothiadiazole species paves the way for the development of novel organic-based sensitizers used in solar cells whose structures are susceptible to modifications. Thus, six novel NR structures were derived from two previously developed structures in laboratories. In this study, density functional theory (DFT) calculations and time-dependent DFT (TD-DFT) were used to determine the optoelectronic properties of the NR-derived moieties such as absorption spectra. Various linkers were investigated in an attempt to understand the impact of π-linkers on the optoelectronic properties. According to the findings, the presence of furan species led to the planarity of the molecule and a reduction

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Publication Date
Fri Jan 01 2016
Journal Name
Journal Of Computer And Communications
Pathfinding in Strategy Games and Maze Solving Using A* Search Algorithm
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Publication Date
Tue Jan 14 2025
Journal Name
South Eastern European Journal Of Public Health
Deep learning-based threat Intelligence system for IoT Network in Compliance With IEEE Standard
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The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre

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