Publications
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Kadhim, Ahmed Khalid; Granmo, Ole-Christoffer; Lei, Jiao & Shafik, Rishad Ahmed
(2024).
Exploring State Space and Reasoning by Elimination in Tsetlin Machines.
In NN, NN (Eds.),
2024 International Symposium on the Tsetlin Machine (ISTM).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9798331504984.
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The Tsetlin Machine (TM) has gained significant attention in Machine Learning (ML). By employing logical fundamentals, it facilitates pattern learning and representation, offering an alternative approach for developing comprehensible Artificial Intelligence (AI) with a specific focus on pattern classification in the form of conjunctive clauses. In the domain of Natural Language Processing (NLP), TM is utilised to construct word embedding and describe target words using clauses. To enhance the descriptive capacity of these clauses, we study the concept of Reasoning by Elimination (RbE) in clauses' formulation, which involves incorporating feature negations to provide a more comprehensive representation. In more detail, this paper employs the Tsetlin Machine Auto-Encoder (TM-AE) architecture to generate dense word vectors, aiming at capturing contextual information by extracting feature-dense vectors for a given vocabulary. Thereafter, the principle of RbE is explored to improve descriptivity and optimise the performance of the TM. Specifically, the specificity parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> and the voting margin parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> are leveraged to regulate feature distribution in the state space, resulting in a dense representation of information for each clause. In addition, we investigate the state spaces of TM-AE, especially for the forgotten/excluded features. Empirical investigations on artificially generated data, the IMDB dataset, and the 20 Newsgroups dataset showcase the robustness of the TM, with accuracy reaching 90.62% for the IMDB.
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Halenka, Vojtech; Kadhim, Ahmed Khalid; Paul, Clarke; Bhattarai, Bimal; Saha, Rupsa & Granmo, Ole-Christoffer
[Show all 8 contributors for this article]
(2024).
Exploring Effects of Hyperdimensional Vectors for Tsetlin Machines.
In NN, NN (Eds.),
2024 International Symposium on the Tsetlin Machine (ISTM).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9798331504984.
doi:
10.1109/ISTM62799.2024.10931546.
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Tsetlin machines (TMs) have been successful in several application domains, operating with high efficiency on Boolean representations of the input data. However, Booleanizing complex data structures such as sequences, graphs, images, signal spectra, chemical compounds, and natural language is non-trivial. In this paper, we propose a hypervector (HV) based method for expressing arbitrarily large sets of concepts associated with any input data. Using a hyperdimensional space to build vectors drastically expands the capacity and flexibility of the TM. We demonstrate how images, chemical compounds, and natural language text are encoded according to the proposed method, and how the resulting HV -powered TM can achieve significantly higher accuracy and faster learning on well-known benchmarks. Our results thus open up a new research direction for TMs, namely how to expand and exploit the benefits of operating in hyperspace, including new booleanization strategies, optimization of TM inference and learning, as well as new TM applications.
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Swadi, Salah M.; Kadhim, Ahmed Khalid & Ali, Ghusoon M.
(2023).
Design of Path Planning Controller of Autonomous Wheeled Mobile Robot Based on Triple Pendulum Behaviour.
International Journal of Mechanical Engineering and Robotics Research (IJMERR).
ISSN 2278-0149.
p. 31–31.
doi:
10.18178/ijmerr.12.1.23-31.
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Tashan, Tariq & Kadhim, Ahmed Khalid
(2022).
PSO based multilevel MRI compression using compressive sensing.
Bulletin of Electrical Engineering and Informatics.
ISSN 2089-3191.
11(5),
p. 2628–2628.
doi:
10.11591/eei.v11i5.3873.
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A multilevel compression method, for magnetic resonance imaging (MRI) images, is presented in this paper. First, the image is segmented into frames of equal size. Then, the sparsity of each frame is computed. Based on the sparsity index value, each frame is compressive sensing (CS) compressed/reconstructed at one level of four. Particle swarm optimization (PSO) is used to optimize the amount of information to be used in the CS reconstruction process, and to optimize the sparsity thresholds, that separate the different compression levels. Two-dimensional sigmoid function is suggested as a fitness function for the PSO. Six MRI images are used to evaluate the performance of the proposed method. The results show considerable gain in both peak signal to noise ratio (PSNR) and compression level (CL), when compared to single level compression, which is commonly considered in the literature.
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Kadhim, Ahmed Khalid; Swadi, Salah M. & Ali, Ghusoon M
(2020).
Design and implementation of a feedback programmable spin coating system.
Indonesian Journal of Electrical Engineering and Computer Science.
ISSN 2502-4752.
17(3),
p. 1516–1516.
doi:
10.11591/ijeecs.v17.i3.pp1516-1523.
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In this work, a speed controlled, the programmable, low-cost, high-efficiency spin coating system is designed and implemented. The system is built from commercially available hard disk drivers DC motor, vacuum pump, speed sensor, brushless driver circuit carries Toshiba's TB6575FNG IC, PIC18F47J53 Microcontroller and using the interactive user interface to communicate with a user. Three different voltage stages and pulse width modulation techniques were used to guarantee accurate spinning speed and time. This system provides a wide range of precise and stable multi-step speeds rang of 1500 to 8,000 rpm. To evaluate the implemented system, ZnO thin films have been deposited by the sol-gel method on a glass substrate. Homogenous and uniform thickness films were obtained. The outstanding features of this system are the stability, accuracy, low energy consumption and durability, and the simplicity of adjusting multi-step spinning speed and time by using the interactive user interface.
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Ali, Ghusoon M.; Kadhim, Ahmed Khalid & Swadi, Salah M.
(2020).
Nanoflower ZnO thin-film grown by hydrothermal technique based Schottky diode.
Journal of Semiconductors.
ISSN 1674-4926.
41(10),
p. 102103–102103.
doi:
10.1088/1674-4926/41/10/102103.
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Abstract This paper reports the realization of planar Schottky diodes based on nanorod ZnO thin film. The nanorod ZnO thin film was fabricated by hydrothermal technique on boron doped p-type Si (100) substrate. The Ag//ZnO/Al planar diode operating with voltage bias from –3 to 3 V. The I – V characteristics clearly indicate that the devices have rectifying performance. The thermionic emission theory governs the current across the studied Schottky diode. The device achieved a turn-on voltage of 0.9 V, barrier height 0.69 eV and saturation current of 1.2 × 10 –6 A. The diode shows a very large ideality factor ( n > > 2) which is attributed to high interface trap concentration. The surface topology was investigated by scanning electron microscope (SEM). The structural properties of the nanostructured ZnO thin film were characterized by X-ray diffraction (XRD). The SEM images reveal that the ZnO nanorods grow perpendicular to the substrate with uniformity and high density. The XRD pattern illustrates the dominant peak appearing at (002). This intense peak indicates the c -axis orientated phase of the wurtzite ZnO structure. It demonstrates that the crystals grow uniformly perpendicular to the substrate surface in good agreement with the SEM images.
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Ali, Ghusoon M.; Moore, James C.; Kadhim, Ahmed Khalid & Thompson, Cody
(2014).
Electrical and optical effects of Pd microplates embedded in ZnO thin film based MSM UV photodetectors: A comparative study.
Sensors and Actuators A-Physical.
ISSN 0924-4247.
209,
p. 23–23.
doi:
10.1016/j.sna.2014.01.010.
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Published
Sep. 1, 2026 5:53 AM
- Last modified
Sep. 1, 2026 5:53 AM