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Artificial Intelligence Based Deep Bayesian Neural Network (DBNN) Toward Personalized Treatment of Leukemia with Stem Cells
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The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of Deep Bayesian Neural Network (DBNN) for the personalized treatment of leukemia cancer has shown a significant tested accuracy for the model. DBNNs used in this study was able to classify images with accuracy exceeding 98.73%. This study depicts that the DBNN can classify cell cultures only based on unstained light microscope images which allow their further use. Therefore, building a bayesian‐based model to great help during commercial cell culturing, and possibly a first step in the process of creating an automated/semiautomated neural network‐based model for classification of good and bad quality cultures when images of such will be available.

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Publication Date
Mon Jan 01 2024
Journal Name
Itm Web Of Conferences
Embedded Neural Network like PID Water Heating Controller Implementing Cycle by Cycle Power Control Scheme
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This paper experimentally investigates the heating process of a hot water supply using a neural network implementation of a self-tuning PID controller on a microcontroller system. The Particle Swarm Optimization (PSO) algorithm employed in system tuning proved very effective, as it is simple and fast optimization algorithm. The PSO method for the PID parameters is executed on the Matlab platform in order to put these parameters in the real-time digital PID controller, which was experimented with in a pilot study on a microcontroller platform. Instead of the traditional phase angle power control (PAPC) method, the Cycle by Cycle Power Control (CBCPC) method is implemented because it yields better power factor and eliminates harmonics

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Publication Date
Wed Feb 21 2024
Journal Name
Al-rafidain Journal Of Medical Sciences ( Issn 2789-3219 )
Perceptions of Senior Pharmacy Students Towards the Impact of Artificial Intelligence on University Education and Scientific Writing: A Qualitative Study
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Background: The roles of AI in the academic community continue to grow, especially in the enhancement of learning outcomes and the improvement of writing quality and efficiency. Objectives: To explore in depth the experience of senior pharmacy students in using artificial intelligence for academic purposes. Methods: This qualitative study included face-to-face individual interviews with senior pharmacy students from March to May 2023 using a pre-planned interview guide of open-ended questions. All interviews were audio-recorded. Thematic analysis was used to analyze the data. Results: The results were obtained from 15 in-depth face-to-face interviews with senior pharmacy students (5th and 4th years). Eight participants were male, and seven

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Publication Date
Sun Mar 01 2015
Journal Name
Baghdad Science Journal
Evaluation of Electrolytes Disturbances in Iraqi Chronic Myeloid Leukemia Patients treated with Nilotinib with Monitoring of Response by FISH Study
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Chronic myeloid leukemia (CML) is a myeloproliferative disorder characterized by the presence Philadelphia chromosome (Ph) which was created by a reciprocal translocation between chromosomes 9 and 22 (t [9;22] [q34;q11]. The approval of the 2nd generation TKI ( Nilotinib) takes the treatment of CML patients into new erea with more efficiency and mild to moderate adverse effects. This study was aimed at evaluation of molecular cytogenetic response by (FISH) for Nilotinib in Iraqi patients with assessment for electrolytes disturbances of Nilotinb by measuring a panel of electrolyte (Na+, K+, Ca++, PO4--- and Mg++) , where thirty Iraqi patients with CML who have resistance or no response to Imatinib treatment, attending to Baghdad Teaching Ho

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Publication Date
Fri Dec 30 2022
Journal Name
Eastern-european Journal Of Enterprise Technologies
Experimental investigation and modelling of residual stresses in face milling of Al-6061-T3 using neural network
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Milling process is a common machining operation that is used in the manufacturing of complex surfaces. Machining-induced residual stresses (RS) have a great impact on the performance of machined components and the surface quality in face milling operations with parameter cutting. The properties of engineering material as well as structural components, specifically fatigue life, deformation, impact resistance, corrosion resistance, and brittle fracture, can all be significantly influenced by residual stresses. Accordingly, controlling the distribution of residual stresses is indeed important to protect the piece and avoid failure. Most of the previous works inspected the material properties, tool parameters, or cutting parameters, bu

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Publication Date
Thu Dec 01 2022
Journal Name
Baghdad Science Journal
Steganography and Cryptography Techniques Based Secure Data Transferring Through Public Network Channel
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Attacking a transferred data over a network is frequently happened millions time a day. To address this problem, a secure scheme is proposed which is securing a transferred data over a network. The proposed scheme uses two techniques to guarantee a secure transferring for a message. The message is encrypted as a first step, and then it is hided in a video cover.  The proposed encrypting technique is RC4 stream cipher algorithm in order to increase the message's confidentiality, as well as improving the least significant bit embedding algorithm (LSB) by adding an additional layer of security. The improvement of the LSB method comes by replacing the adopted sequential selection by a random selection manner of the frames and the pixels wit

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Publication Date
Mon Oct 02 2023
Journal Name
Journal Of Engineering
Microgrid Integration Based on Deep Learning NARMA-L2 Controller for Maximum Power Point Tracking
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This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength.  This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.

Moreover, the proposed controller i

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Publication Date
Wed Nov 01 2017
Journal Name
Journal Of Computational And Theoretical Nanoscience
Solution for Multi-Objective Optimisation Master Production Scheduling Problems Based on Swarm Intelligence Algorithms
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The emphasis of Master Production Scheduling (MPS) or tactic planning is on time and spatial disintegration of the cumulative planning targets and forecasts, along with the provision and forecast of the required resources. This procedure eventually becomes considerably difficult and slow as the number of resources, products and periods considered increases. A number of studies have been carried out to understand these impediments and formulate algorithms to optimise the production planning problem, or more specifically the master production scheduling (MPS) problem. These algorithms include an Evolutionary Algorithm called Genetic Algorithm, a Swarm Intelligence methodology called Gravitational Search Algorithm (GSA), Bat Algorithm (BAT), T

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Publication Date
Wed Jan 25 2023
Journal Name
Molecular Simulation
Engineering promising A-π-D type molecules for efficient organic-based material solar cells
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Within this work, to promote the efficiency of organic-based solar cells, a series of novel A-π-D type small molecules were scrutinised. The acceptors which we designed had a moiety of N, N-dimethylaniline as the donor and catechol moiety as the acceptor linked through various conjugated π-linkers. We performed DFT (B3LYP) as well as TD-DFT (CAM-B3LYP) computations using 6-31G (d,p) for scrutinising the impact of various π-linkers upon optoelectronic characteristics, stability, and rate of charge transport. In comparison with the reference molecule, various π-linkers led to a smaller HOMO–LUMO energy gap. Compared to the reference molecule, there was a considerable red shift in the molecules under study (A1–A4). Therefore, based on

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Publication Date
Sat Jan 05 2019
Journal Name
Iraqi Journal Of Physics
Solar cells based on natural dyes prepared using anatase phase titanium dioxide
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Natural dye sensitized solar cell was prepared using strawberry and pomegranate dyes with anatase nanocrystalline titanium dioxide powder. A study of the optical properties of the two dyes, involving the absorption spectrum was determined in the visible region. I-V characteristics under illumination were performed. The results showed that the two prepared dye sensitized solar cells have acceptable values efficiency about (0.94 with Fill factor (45)) and (0.74 with Fill factor (44)) for strawberry and pomegranate dyes, respectively.

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Publication Date
Mon Mar 31 2025
Journal Name
International Journal Of Advanced Technology And Engineering Exploration
Breast cancer survival rate prediction using multimodal deep learning with multigenetic features
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Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep

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