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International Symposium on Computational Intelligence and Design, ISCID

作者： Chunxia Jin Su Zhang Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China Department of Management Science Taiwan Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9781728146539

ISBN: (纸本)9781728146546

In micro-blog short text clustering, the amount of text information contained in micro-blog short text is small, with timeliness, sparseness and singularity, so the artificial selection of attribute words has some limitations. This paper puts forward the feature extraction of text information in micro-blog using Bootstrapping algorithm, it can choose the higher theme information reflect the characteristics of words, and then use the improved TFIDF algorithm to calculate the weight of micro-blog based text clustering. Finally, K-means clustering algorithm is used to cluster micro-blog short text. The experimental results show that the clustering algorithm of micro-blog short text proposed in this paper is better than other algorithms, which improves the clustering effect of micro-blog short text.

关键词： Clustering algorithms Feature extraction Machine learning algorithms Data mining Blogs Classification algorithms Probabilistic logic

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Toward a Way to Benchmark Multilevel Inverter Topologies Based on Level to Components Ratio

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Canadian Journal of Electrical and computer engineering 2019年第2期42卷 78-92页

作者： Sheir, Ahmed Orabi, Mohamed Youssef, Mohamed Z. Ahmed, Mahrous Department of Electrical Computer and Software Engineering Power Electronics and Drives Applications Lab School of Engineering and Applied Science University of Ontario Institute of Technology OshawaONL1H 1K7 Canada Department of Electrical Engineering Aswan Power Electronics Applications Research Center Faculty of Engineering Aswan81542 Egypt

This paper introduces a generalized platform for the design selection of multilevel inverters (MLIs) topologies for any industry, especially in low-and medium-power applications. This platform is novel as it combines the component count and stress in any MLI in a normalized unique score. Each MLI topology is designed to match specific criteria to meet a specific application, which introduces diversified sets of merits and demerits in each case. Therefore, choosing a suitable MLI topology for the specific application is rather a difficult, time-consuming, and complex process. Moreover, the comparison can only be conducted between MLI topologies that fall under the same category. This paper introduces a comprehensive study to propose a new benchmark (BM) which can be used to compare MLI topologies regardless of their constructions or oriented applications. Each MLI topology gets a BM score based on comparative factors that evaluate its components quantitatively and qualitatively. Thus, comparing different types of MLI topologies is a simple process of comparing their calculated BM scores leading to better well-informed choice. First, the proposed BM-derived forms are discussed. Then, the proposed methodology is illustrated by applying to various types of MLI topologies including conventional and recently introduced ones. © 2003-2010 IEEE Canada.

关键词： Topology

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

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A mobile application for crowdsourced road condition monitoring 4

A mobile application for crowdsourced road condition monitor...

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4th International Conference on Information technology Research, ICITR 2019

作者： Kumara Thilakarathna, Thusithanjana Kavinda Perera, Hasith E Jayaweera, H H E Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka University of Colombo Department of Physics Colombo 03 Sri Lanka

ISBN: (纸本)9781728161990

An Android App has been developed to measure geo-tagged vehicle-induced vibrations with the use of the built-in accelerometer of a mobile phone. The architecture of the app is designed in such a way that it can be published as a crowdsourced vehicle-induced vibration measurement data collection app. A simple calibration method has been developed to calibrate each individual axis of the accelerometer to get rid of imperfections within the three axes. The system is evaluated for extracting data of vibrations for a known system (simple pendulum) and the same system was tested for vehicle induced-vibration capturing. The results produced from the system during the test run correctly classified different road conditions. Once the crowdsourced data is available, a more statistically significant prediction could be performed. © 2019 IEEE.

关键词： Accelerometers

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IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive Computing (Plenary Panel Report-II) 20

IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive...

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20th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2021

作者： Kinsner, Witold Zhu, Haibin Baciu, George Luo, Guiming Rubio, Fernando Sui, Jie Huang, Runhe Hiraishi, Hironori Peng, Jun Chen, Liang Univ. of Manitoba FI2CICC Dept of ECE Winnipeg Canada Nipissing University FI2CICC ON Canada Hong Kong Polytechnic University FI2CICC Dept. of Computing Hong Kong Tsinghua University FI2CICC Software Institute Beijing China Complutense Univ. of Madrid FI2CICC Dept. of Computer Systems and Computation Spain Aberdeen University FI2CICC Aberdeen United Kingdom Hosei University FI2CICC Faculty of Computer and Information Sciences Japan Ashikaga University Faculty of Engineering Tochigi326-8558 Japan Chongqing Univ. of Science and Technology FI2CICC School of AI Chongqing China Univ. of Northern British Columbia Dept. of Computer Science BC Canada

ISBN: (纸本)9781665421195

Cognitive Computing (CC) is a contemporary field of fundamental intelligence theories and general AI technologies triggered by the transdisciplinary development in intelligence, computer, brain, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary of the plenary panel (Part II) on the theoretical foundations of CI/CC and recent breakthroughs in AI engineering reported in the 20th IEEE International ICCI*CC Conference (ICCI*CC'21). The latest advances in CI and CC towards general AI are presented by twenty-Two distinguished panelists. Strategic AI engineering applications in CI, CC, and cognitive systems are elaborated for abstract intelligence, cognitive robots, autonomous systems, intelligent vehicles, and safety-And-mission-critical systems. © 2021 IEEE.

关键词： Cognitive systems

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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A novel method for breast mass segmentation: from superpixel to subpixel segmentation

A novel method for breast mass segmentation: from superpixel...

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作者： Gu, Shenghua Chen, Yi Sheng, Fangqing Zhan, Tianming Chen, Yunjie School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science and Technology Nanjing Normal University Nanjing210023 China Jiangsu Laboratory of Lake Environment Remote Sensing Technologies Huaiyin Institute of Technology Huaiyin223001 China Faculty of Hospitality and Tourism Management Macau University of Science and Technology Macau China School of Humanity and Art Jiangsu Maritime Institute Nanjing211100 China School of Information Engineering Nanjing Audit University Nanjing211815 China School of Math and Statistics Nanjing University of Information Science and Technology Nanjing210044 China

In this paper, an effective method is proposed for breast mass segmentation using a superpixel generation and curve evolution method. The simple linear iterative clustering method and density-based spatial clustering of applications with noise method are applied to generate superpixels in mammograms at first. Thereafter, a region of interesting (ROI) that contains the breast mass is built on the superpixel generation results. Finally, the image patch and the position of the manual labeled seed are used to build the prior knowledge for the level set method driven by the local Gaussian distribution fitting energy and evolve the curve to capture the edge of breast mass in ROI. Experimental results on mammogram data set demonstrate that the proposed method shows superior performance in contrast to some well-known methods in breast mass segmentation. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Gaussian distribution

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CRNN based jersey-bib number/text recognition in sports and marathon images 15

CRNN based jersey-bib number/text recognition in sports and ...

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15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Nag, Sauradip Ramachandra, Raghavendra Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Kankanhalli, Mohan Department of Computer Science & Engineering Kalyani Government Engineering College Kalyani India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Norway Faculty of Computer System and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University China Department of Computer Science School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9781728128610

The primary challenge in tracing the participants in sports and marathon video or images is to detect and localize the jersey/Bib number that may present in different regions of their outfit captured in cluttered environment conditions. In this work, we proposed a new framework based on detecting the human body parts such that both Jersey Bib number and text is localized reliably. To achieve this, the proposed method first detects and localize the human in a given image using Single Shot Multibox Detector (SSD). In the next step, different human body parts namely, Torso, Left Thigh, Right Thigh, that generally contain a Bib number or text region is automatically extracted. These detected individual parts are processed individually to detect the Jersey Bib number/text using a deep CNN network based on the 2-channel architecture based on the novel adaptive weighting loss function. Finally, the detected text is cropped out and fed to a CNN-RNN based deep model abbreviated as CRNN for recognizing jersey/Bib/text. Extensive experiments are carried out on the four different datasets including both bench-marking dataset and a new dataset. The performance of the proposed method is compared with the state-of-the-art methods on all four datasets that indicates the improved performance of the proposed method on all four datasets. © 2019 IEEE.

关键词： Sports

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An energy-aware distributed open market model for UAV-assisted communications

An energy-aware distributed open market model for UAV-assist...

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IEEE Conference on Vehicular technology (VTC)

作者： Rafay Iqbal Ansari Nouman Ashraf Christos Politis School of Computer Science and Mathematics Faculty of Science Engineering and Computing Kingston University London United Kingdom Telecommunications Software and Systems Group (TSSG) Waterford Institute of Technology Waterford Ireland.

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

Unmanned aerial vehicles (UAVs) have opened up numerous opportunities in terms of connectivity, especially in the context of realizing the vision of ubiquitous connectivity for 5G and beyond (B5G). The ease of mobility makes the UAV base stations (UAV-BSs) a viable candidate for providing `on demand' services to the users. Moreover, viewing the spectrum crunch experienced by traditional cellular networks, the UAV-BSs can share the burden of providing connectivity. UAV-BSs can open up several business opportunities for mobile network operators (MNOs). In this paper, we propose an open market model, where a UAV-BS has the opportunity to establish a link with a terrestrial BS (TBS) of an MNO that provides the best connectivity and offers a lower price. A distributed model is considered where the decision making power lies with the UAV-BS. The TBS-selection problem is modeled as an integer linear programming problem, where we compare the performance of the Greedy heuristic algorithm (GHA) and the backtracking algorithm (BA) to solve our selection problem. We also incorporate an energy prediction model which impacts the selection criteria. We analyze the performance GHA and BA algorithm by presenting a tradeoff between the two algorithms in terms of accuracy of TBS selection and convergence time.

关键词： Unmanned aerial vehicles Prediction algorithms Optimization Base stations Heuristic algorithms Predictive models Standardization

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Semantic-Preserving Adversarial Text Attacks

TechRxiv

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TechRxiv 2021年

作者： Yang, Xinghao Gong, Yongshun Liu, Weifeng Bailey, James Zhu, Tianqing Tao, Dacheng Liu, Wei The School of Computer Science University of Technology Sydney 15 Broadway UltimoNSW2007 Australia The School of Software Shandong University Jinan China Qingdao266580 China The Department of Computing and Information Systems University of Melbourne Australia The JD Explore Academy China The Digital Science Institute Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia

Deep learning models are known immensely brittle to adversarial image examples, yet their vulnerability in text classification is insufficiently explored. Existing text adversarial attack strategies can be roughly divided into three categories, i.e., character-level attack, word-level attack, and sentence-level attack. Despite the success brought by recent text attack methods, how to induce misclassification with the minimal text modifications while keeping the lexical correctness, syntactic soundness, and semantic consistency simultaneously is still a challenge. To examine the vulnerability of deep models, we devise a Bigram and Unigram based adaptive Semantic Preservation Optimization (BU-SPO) approach which attacks text documents not only at a unigram word level but also at a bigram level to avoid generating meaningless sentences. We also present a hybrid attack strategy that collects substitution words from both synonyms and sememe candidates, to enrich the potential candidate set. Besides, a Semantic Preservation Optimization (SPO) method is devised to determine the word substitution priority and reduce the perturbation cost. Furthermore, we constraint the SPO with a semantic Filter (dubbed SPOF) to improve the semantic similarity between the input text and the adversarial example. To estimate the effectiveness of our proposed methods, BU-SPO and BU-SPOF, we attack four victim deep learning models trained on three real-world text datasets. Experimental results demonstrate that our approaches accomplish the highest semantics consistency and attack success rates by making the minimal word modifications compared with competitive methods. © 2021, CC BY.

关键词： Semantics

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