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arXiv 2022年

作者： Amin, Askandar H. Ahmed, Botan K. Maaroof, Bestan B. Rashid, Tarik A. Technical College of Informatics Sulaimani Polytechnic University KRG Sulaimani Iraq Administration and Economics University of Sulaimani KRG Sulaimani Iraq IT Department College of Commerce University of Sulaimani KRG Sulaimani Iraq School of Information Technology Fanshawe College London Canada Computer Science and Engineering Dept. University of Kurdistan Hewler KRG Erbil Iraq

The prior detection of a heart attack could lead to the saving of one’s life. Putting specific criteria into a system that provides an early warning of an imminent attack will be advantageous to a better prevention plan for an upcoming heart attack. Some studies have been conducted for this purpose, but yet the goal has not been reached to prevent a patient from getting such a disease. In this paper, Neural Network trained with Particle Swarm Optimization (PSONN) is used to analyze the input criteria and enhance heart attack anticipation. A real and novel dataset that has been recorded on the disease is used. After preprocessing the data, the features are fed into the system. As a result, the outcomes from PSONN have been evaluated against those from other algorithms. Decision Tree, Random Forest, Neural network trained with Backpropagation (BPNN), and Naive Bayes were among those employed. Then the results of 100%, 99.2424%, 99.2323%, 81.3131%, and 66.4141% are produced concerning the mentioned algorithms, which show that PSONN has recorded the highest accuracy rate among all other tested algorithms. © 2022, CC BY.

关键词： Swarm intelligence

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Application of internet + big data and artificial intelligence in vocational education 4

Application of internet + big data and artificial intelligen...

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4th International Conference on Information Systems engineering, ICISE 2019

作者： Zeng, Wenying Kang, Siqi Li, Binning Computer Engineering Technical College Guangdong Polytechnic of Science and Technology Jinwan District Zhuhai China Academy of International Cooperation Guangdong Polytechnic of Science and Technology Jinwan District Zhuhai China Guangdong Institute of Talents Guangdong Polytechnic of Science and Technology Kehua Street Tianhe District Guangzhou China

ISBN: (纸本)9781728125589

Internet + platforms lead the application of big data and artificial intelligence to ordinary users, with reduced costs of technology. Meanwhile, they have driven professional education to update courses and promote efficiency of talents cultivation. The curriculum system of vocational education needs to consider the application of new technologies comprehensively to ensure the availability of knowledge and ability structure of personnel training. To achieve the goal, the application methods and approaches of teaching and practice with big data and artificial intelligence in vocational education are explored and researched;practice of innovative application in software technology are illustrated. It is expected to help promote the reform and development of vocational education and speedup cultivation of innovative and entrepreneurial talents. © 2019 IEEE.

关键词： Big data

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Sparse Representation Based Inverse Halftoning with Boosted Dictionary1

Research Square

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Research Square 2021年

作者： Yang, Jun Liu, Zihao Chen, Li Wu, Ying Cui, Chen Ke, Gang College of Information Science and Engineering Jiaxing University Jiaxing China College of Information Engineering Jiaxing Nanhu University Jiaxing China School of Information Science and Technology Heilongjiang University Harbin150080 China Department of Computer Engineering Dongguan Polytechnic Guangdong Province Dongguan China

Halftoning image is widely used in printing and scanning equipment, which is of great significance for the preservation and processing of these images. However, because of the different resolution of the display devices, the processing and display of halftone image are confronted with great challenges, such as Moore pattern and image blurring. Therefore, the inverse halftone technique is required to remove the halftoning screen. In this paper, we propose a sparse representation based inverse halftone algorithm via learning the clean dictionary, which is realized by two steps: deconvolution and sparse optimization in the transform domain to remove the noise. The main contributions of this paper include three aspects: first, we analysis the denoising effects for different training sets and the redundancy of dictionary;Then we propose the improved a sparse representation based denoising algorithm through adaptively learning the dictionary, which iteratively remove the noise of the training set and upgrade the quality of the dictionary;Then the error diffusion halftone image inverse halftoning algorithm is proposed. Finally, we verify that the noise level in the error diffusion linear model is fixed, and the noise level is only related to the diffusion operator. Experimental results show that the proposed algorithm has better PSNR and visual performance than state-of-the-art methods. © 2021, CC BY.

关键词： Inverse problems

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Effect of Fat, Oil and Grease (Fog) on the Conversion of Lignite to Biogenic Methane

SSRN

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SSRN 2022年

作者： Song, Bo Xia, Daping Guo, Hongyu Dong, Zhiwei Wang, Yongjun Su, Xianbo Chen, Zhenhong Zhao, Weizhong School of Energy Science and Engineering Henan Polytechnic University Jiaozuo454000 China Collaborative Innovation Center of Coalbed Methane and Shale Gas for Central Plains Economic Region Jiaozuo454000 China Institute of Engineering Thermophysics School of Energy and Power Engineering Chongqing University Chongqing 400030 China College of Computer Science and Technology Henan Polytechnic University Jiaozuo454000 China Department of Environmental Engineering Technical University of Denmark Lyngby DK-2800 Denmark China National Petroleum Corporation China Henan Polytechnic University China

The biogenic methane production experiment of FOG combined with lignite was performed to enhance the methane output of lignite anaerobic fermentation. Three-dimensional fluorescence, gas chromatography-mass spectrometry, volatile fatty acids and 16S rDNA tests were carried out to analyze the composition of the liquid substances at the biomethane peak production. The results showed that the yield of this compound in the combined fermentation of lignite and FOG could be increased by 380.25%. Moreover, the degradable organic matter and acetic acid content increased, while the degree of humification decreased. In addition, the microbial activity was enhanced. Acinetobacter and Methanosarcina were activated as dominant bacteria in the combined substrate of the anaerobic fermentation process, which improved the degradation efficiency of organic matter and the overall biomethane yield. This research showed that the combined anaerobic fermentation of FOG and lignite could promote the biomethane production efficiency in the context of anaerobic fermentation, which provided a new idea for the clean transformation of lignite and utilization of FOG. © 2022, The Authors. All rights reserved.

关键词： Mass spectrometry

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Management of business using machine learning for decision making

Management of business using machine learning for decision m...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Saud Ilahi Bestoon Othman Kapil Joshi S. Sundararajan Patel Yogeshkumar Jethabhai Veer Sudheer Gour Department of Business Administration College of Business Administration Jazan University Jazan KSA Business Administration Koya Technical Institute Erbil Polytechnic University Erbil Iraq Department of Business Administration College of Administration and Economics Nawroz University Duhok Iraq Uttaranchal Institute of Technology Uttaranchal University Business Management Skyline University NigeriaKano City Kano State Nigeria Faculty of Computer Science Shri C.J. Patel College of Computer Studies Sankalchand Patel University Department of CSE St. Martin's Engineering College Secunderabad Telangana India

While machine learning and artificial intelligence might lessen expenses and increment the viability of business processes, these methods can likewise obliterate the worth of an organization, some of the time with serious repercussions. The failure to perceive and control that chance might make a few directors put off carrying out innovations, keeping them from arriving at their maximum capacity. Machine learning has colossal potential for decreasing the expense of labor and products, speeding up organization activities, and further developing client care. In the ongoing time of uncommon specialized headway, it is recognized as one of the huge application regions, and reception is advancing across essentially all businesses. In light of this, Researchers first provide a brief review of the different types of machine learning before presenting three different ways that machine learning is used in businesses. The trade-off between machine-learning algorithms' accuracy and interpretability is then covered. This is an important factor to take into account while choosing the best approach for the task at hand.

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Fitness dependent optimizer with neural networks for COVID-19 patients

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computer Methods and Programs in Biomedicine Update 2023年 3卷 100090页

作者： Abdulkhaleq, Maryam T. Rashid, Tarik A. Hassan, Bryar A. Alsadoon, Abeer Bacanin, Nebojsa Chhabra, Amit Vimal, S. Department of Computer Science and Engineering University of Kurdistan Hewler KRI Iraq Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani KRI Iraq Department of Computer Networks Technical College of Informatics Sulaimani Polytechnic University Sulaimani KRI Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Information Technology Department Asia Pacific International College (APIC) Sydney Australia Singidunum University Danijelova 32 Belgrade 11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Dept of CSE Ramco Institute of Technology Tamilnadu Rajapalayam India

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected the global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the Covid 19 models is found here: https://***/Tarik4Rashid4/covid19models © 2022

关键词： COVID 19 FDO Fitness dependent optimizer Machine learning Swarm intelligence

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Advancing agriculture with machine learning:a new frontier in weed management

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Frontiers of Agricultural science and engineering 2025年第2期 288-307页

作者： Mohammad MEHDIZADEH Duraid K.A.AL-TAEY Anahita OMIDI Aljanabi Hadi Yasir ABBOOD Shavan ASKAR Soxibjon TOPILDIYEV Harikumar PALLATHADKA Renas Rajab ASAAD Department of Agronomy and Plant Breeding Faculty of Agriculture and Natural Resources University of Mohaghegh Ardabili Ilam Science and Technology Park Department of Horticulture College of Agriculture University of Al-Qasim Green Department of GIS and Remote Sensing Faculty of Geography University of Tehran Medical Laboratories Techniques Department Al-Mustaqbal University College Erbil polytechnic university Technical engineering college information system engineering department Department of Fundamental Economics Tashkent State University of Economics Manipur International University Department of Computer Science Nawroz University

Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability. Long-established methods of weed control, such as manual labor and synthetic herbicides, have been widely used but come with their own set of challenges. These methods are often time-consuming, labor-intensive, and pose environmental *** have been the primary method of weed control due to their efficiency and cost-effectiveness. However, over-reliance on herbicides has led to environmental contamination, weed resistance, and potential health hazards. To address these issues, researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies. As technology advances, there is a growing interest in exploring innovative and sustainable weed management approaches. This review examines the potential of machine learning in chemical weed *** learning offers innovative and sustainable approaches by analyzing large data sets, recognizing patterns, and making accurate *** learning models can classify weed species and optimize herbicide usage. Real-time monitoring enables timely intervention, preventing invasive species spread. Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices, reducing herbicide usage and minimizing environmental impact. Validation and refinement of these algorithms are needed for practical application.

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In-Situ Fluorine-Doped Znsno Thin Film and Thin-Film Transistor

SSRN

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SSRN 2023年

作者： Yin, Xuemei Lin, Delang Zhong, Wei Deng, Sunbin Xu, Yuming Chen, Yayi Lu, Lei Li, Guijun Kwok, Hoi Sing Li, Yi Chen, Rongsheng The School of artificial intelligence Zhuhai City Polytechnic College Zhuhai China The School of Microelectronics South China University of Technology Guangzhou China The School of Integrated Circuits Guangdong University of Technology Guangzhou China Purdue University West Lafayette United States The State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The Hong Kong University of Science and Technology Hong Kong Zhuhai Southern Integrated Circuit Design Service Center Zhuhai China Peking University China

In this paper, in-situ fluorine-doped ZnSnO (ZTO:F) thin films prepared by co-sputtering are proposed. It was found that the F had been successfully introduced into the ZTO thin films and distributed uniformly in the bulk. The corresponding top-contact bottom-gated ZTO:F thin film transistors (TFTs) were fabricated with an annealing temperature of 350 oC. The fabricated TFTs exhibit a field-effect mobility of 14.2 cm2V-1s-1, on-off ratio of over 109, and subthreshold swing (SS) as low as 87 mV/decade due to the incorporation of F. In addition, the bias stability of fabricated TFTs was also examined and the Vth shifts under negative gate bias stress are not more than ±0.1V even without any passivation. It is expected that the In-free ZTO:F amorphous oxide semiconductor and the corresponding TFTs have potential for low-cost and environmentally safe fabrication. © 2023, The Authors. All rights reserved.

关键词： Thin film transistors

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Reducing Formal Context Extraction: A Newly Proposed Framework from Big Corpora

arXiv

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arXiv 2025年

作者： Hassan, Bryar A. Qader, Shko M. Hassan, Alla A. Lu, Joan Ahmed, Aram M. Majidpour, Jafar Rashid, Tarik A. Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Erbil Iraq Department of Computer Science College of Science Charmo University Chamchamal KRI Sulaimani46023 Iraq Information Technology Department Kalar Technical Institute Garmian Polytechnic University Sulaimani Iraq Department of Database Technology Computer Science Institute Sulaimani Polytechnic University Sulaimani Iraq Department of Computer Science School of Computing and Engineering University of Huddersfield Queensgate HuddersfieldHD1 3DH United Kingdom Department of Computer Science University of Raparin Rania Iraq

Automating the extraction of concept hierarchies from free text is advantageous because manual generation is frequently labor- and resource-intensive. Free result, the whole procedure for concept hierarchy learning from free text entails several phases, including sentence-level text processing, sentence splitting, and tokenization. Lemmatization is after formal context analysis (FCA) to derive the pairings. Nevertheless, there could be a few uninteresting and incorrect pairings in the formal context. It may take a while to generate formal context;thus, size reduction formal context is necessary to weed out irrelevant and incorrect pairings to extract the concept lattice and hierarchies more quickly. This study aims to propose a framework for reducing formal context in extracting concept hierarchies from free text to reduce the ambiguity of the formal context. We achieve this by reducing the size of the formal context using a hybrid of a WordNet-based method and a frequency-based technique. Using 385 samples from the Wikipedia corpus and the suggested framework, tests are carried out to examine the reduced size of formal context, leading to concept lattice and concept hierarchy. With the help of concept lattice-invariants, the generated formal context lattice is compared to the normal one. In contrast to basic ones, the homomorphic between the resultant lattices retains up to 98% of the quality of the generating concept hierarchies, and the reduced concept lattice receives the structural connection of the standard one. Additionally, the new framework is compared to five baseline techniques to calculate the running time on random datasets with various densities. The findings demonstrate that, in various fill ratios, hybrid approaches of the proposed method outperform other indicated competing strategies in concept lattice performance. © 2025, CC BY.

关键词： Text mining

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A Novel Cluster Detection of COVID-19 Patients and Medical Disease Conditions Using Improved Evolutionary Clustering Algorithm Star

arXiv

引用

arXiv 2021年

作者： Hassan, Bryar A. Rashid, Tarik A. Hamarashid, Hozan K. Department of Computer Networks Technical College of Informatics Sulaimani Polytechnic University Sulaimani46001 Iraq Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani46001 Iraq Computer Science and Engineering Department University of Kurdistan Hewler Iraq Information Technology Department Computer Science Institute Sulaimani Polytechnic University Sulaimani46001 Iraq

With the increasing number of samples, the manual clustering of COVID-19 and medical disease data samples becomes time-consuming and requires highly skilled labour. Recently, several algorithms have been used for clustering medical datasets deterministically;however, these definitions have not been effective in grouping and analysing medical diseases. The use of evolutionary clustering algorithms may help to effectively cluster these diseases. On this presumption, we improved the current evolutionary clustering algorithm star (ECA*), called iECA*, in three manners: (i) utilising the elbow method to find the correct number of clusters;(ii) cleaning and processing data as part of iECA* to apply it to multivariate and domain-theory datasets;(iii) using iECA* for real-world applications in clustering COVID-19 and medical disease datasets. Experiments were conducted to examine the performance of iECA* against state-of-the-art algorithms using performance and validation measures (validation measures, statistical benchmarking, and performance ranking framework). The results demonstrate three primary findings. First, iECA* was more effective than other algorithms in grouping the chosen medical disease datasets according to the cluster validation criteria. Second, iECA* exhibited the lower execution time and memory consumption for clustering all the datasets, compared to the current clustering methods analysed. Third, an operational framework was proposed to rate the effectiveness of iECA* against other algorithms in the datasets analysed, and the results indicated that iECA* exhibited the best performance in clustering all medical datasets. Further research is required on real-world multi-dimensional data containing complex knowledge fields for experimental verification of iECA* compared to evolutionary algorithms. © 2021, CC BY-NC-ND.

关键词： Coronavirus

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