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Restoring the missing features of the corrupted speech using linear interpolation methods

AIP Conference Proceedings 2017年第1期1891卷

作者： Taha H. Rassem Nasrin M. Makbol Ali Muttaleb Hasan Siti Syazni Mohd Zaki P. N. Girija 1Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Malaysia 2School of Electrical and Electronic Engineering Universiti Sains Malaysia Penang Malaysia 3School of Computer and Information Sciences UoH Hyderabad T.S India

One of the main challenges in the Automatic Speech Recognition (ASR) is the noise. The performance of the ASR system reduces significantly if the speech is corrupted by noise. In spectrogram representation of a speech signal, after deleting low Signal to Noise Ratio (SNR) elements, the incomplete spectrogram is obtained. In this case, the speech recognizer should make modifications to the spectrogram in order to restore the missing elements, which is one direction. In another direction, speech recognizer should be able to restore the missing elements due to deleting low SNR elements before performing the recognition. This is can be done using different spectrogram reconstruction methods. In this paper, the geometrical spectrogram reconstruction methods suggested by some researchers are implemented as a toolbox. In these geometrical reconstruction methods, the linear interpolation along time or frequency methods are used to predict the missing elements between adjacent observed elements in the spectrogram. Moreover, a new linear interpolation method using time and frequency together is presented. The CMU Sphinx III software is used in the experiments to test the performance of the linear interpolation reconstruction method. The experiments are done under different conditions such as different lengths of the window and different lengths of utterances. Speech corpus consists of 20 males and 20 females; each one has two different utterances are used in the experiments. As a result, 80% recognition accuracy is achieved with 25% SNR ratio.

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AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

arXiv

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arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson Rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

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Optimal medicinal cupping points selection for asthma disease via graph colouring: a preliminary study

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Journal of Physics: Conference Series 2019年第1期1358卷

作者： Yuhani binti Yusof Mohd Shambin Mohamad Noraziah Adzhar Nurfatihah Mohamad Hanafi Marta Elizabeth Muhamad Faiz Abu Bakar Mohd Adhha Ibrahim AIMs Research Cluster Faculty of Industrial Sciences & Technology Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Kuantan Pahang Malaysia Pusat Bekam Al-Yakin (L***) Kuantan Pahang Malaysia

Medicinal cupping is a traditional therapy which used by applying a cup on acu-points or cupping points and the pressure inside the cup is reduced so that the skin and superficial muscle layer is drawn into and held in the cup. Since all diseases and pains have their specific area to be cupped, most cupping practitioners usually recognize the cupping points based upon the disease and the patient's complaints. Hence, they will randomly choose the points upon request due to the money constraint. However, there is no mathematical approach on guaranteeing the handy method is optimized. Thus, in this paper, a graph model is proposed on finding the optimal number of cupping points for asthma disease via graph colouring approach. This mathematical model will further benefit to the biomathematics and medical fields, especially to the industrial cupping practitioners.

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Multi-objective model optimisation using genetic algorithms for pleurotus sp. cultivation

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IOP Conference Series: Materials Science and engineering 2020年第1期863卷

作者： N Zainol A S Fakharudin N A Dzulkefli M F A Bakar CARIFF Universiti Malaysia Pahang 26300 Gambang Pahang Malaysia. Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang 26300 Gambang Pahang Malaysia. Faculty of Chemical & Natural Resources Engineering Universiti Malaysia Pahang 26300 Gambang Pahang Malaysia.

Malaysia is the largest exporter of Elaeis Guineensis (Palm oil) in the international market. Oil palm cultivation generates a significant amount of lignocellulosic biomass derived from empty fruit bunches (EFB) as waste product. This research focused on optimizing the mycelium growth in Pleurotus sp. cultivation by using EFB as a culture medium. The EFB was cut into the range of size of substrate (S) from 1.5 cm to 3.0 cm, soaked in water for overnight, applied steam treatment and incubated at the selected range of temperature (T) from 29 °C to 32 °C. The responses were mycelium extension rate (M) and nitrogen concentration in mycelium (N). The multi-objective optimisation of M and N requires the objective functions which represent both processes. For this type of problem, multi-objective genetic algorithm was chosen as the methodology, specifically using NSGA-II algorithm. Through the implementation of selected multi-objective genetic algorithm, it was able to produce the pareto front for optimising both nitrogen concentration and the extension rate of the mycelium. The highest nitrogen concentration and mycelium extension rate was from the result with crossover and mutation probability of 0.5 and 0.2. It produced 388.45 mg/L of nitrogen concentration and 0.370 cm/day of mycelium growth.

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Correction to: Feature selection and risk prediction for patients with coronary artery disease using data mining

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Medical & biological engineering & computing 2022年第3期60卷 887页

作者： Nashreen Md Idris Yin Kia Chiam Kasturi Dewi Varathan Wan Azman Wan Ahmad Kok Han Chee Yih Miin Liew Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia. Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia. yinkia@um.edu.my. Department of Information Systems Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia. Department of Medicine University Malaya Medical Centre 50603 Kuala Lumpur Malaysia. Department of Biomedical Engineering Faculty of Engineering Universiti Malaya 50603 Kuala Lumpur Malaysia.

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Royal Academy of Medicine in Ireland - Section of Bioengineering - Proceedings of meeting held in Dundalk, Co. Louth, in February 1998.

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IRISH JOURNAL OF MEDICAL SCIENCE 1998年第4期167卷 256-276页

作者： [Anonymous] 1. Orthopaedic Research Unit The Queen’s University of Belfast Musgrave Park Hospital Belfast 2. School of Biomedical Sciences The Queen’s University of Belfast Medical Biology Centre Belfast 3. Howmedica International Ltd. Raheen Industrial Estate Limerick 4. Department of Mechanical Engineering Trinity College Dublin 2 5. Mater Hospital Eccles Street Dublin 7 6. Orthopaedic Research Laboratory ?rhus University Hospital Arhus Denmark 7. University of Limerick Plassey Technological Park Limerick 8. Limerick Regional Hospital Raheen Limerick 9. Boston Scientific Ireland Ltd. Ballybrit Business Park Galway 10. Department of Mechanical Engineering University College Dublin Dublin 11. Department of Electrical and Electronic Engineering University College Dublin Dublin 12. Bioengineering Research Centre Department of Mechanical Engineering University College Dublin Dublin 13. Northern Ireland Bio-Engineering Centre (NIBEC) School of Electrical and Mechanical Engineering University of Ulster Shore Road BT37 OQB Newtownabbey Co. Antrim 14. Department of Mechanical Engineering University College Dublin Dublin 15. Department of Mechanical & Manufacturing Engineering The Ashby Building The Queen’s University of Belfast Belfast 16. Johnson and Johnson Professional Orthopaedic Division Stem Lane Queensway New Milton England 17. Department of Cardiology and Department of Medical Physics & Bioengineering St. James’s Hospital Dublin 8 18. Burke House Ellison Street Castlebar Co. Mayo 19. Royal College of Surgeons in Ireland Department of Surgery Beaumont Hospital Dublin 20. Department of Mechanical Engineering Trinity College Dublin 2 21. Department of Electronic and Electrical Engineering Trinity College Dublin 2 22. Department of Aeronautical Engineering The Queen’s University of Belfast Belfast 23. Vascular Unit Belfast City Hospital Belfast 24. Department of Medical Physics and Bioengineering St. James’s Hospital Dublin 8 25. Department of Electronic and Electric

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Preface

Studies in Big Data

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Studies in Big Data 2021年 73卷 v-vii页

作者： Chakraborty, Chinmay Banerjee, Amit Kolekar, Maheshkumar H. Garg, Lalit Chakraborty, Basabi Department of Electronics and Communication Engineering Birla Institute of Technology Ranchi Mesra India Department of Electrical Engineering Indian Institute of Technology Patna Patna India Faculty of Software and Information Science Iwate Prefectural University Takizawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta Msida Malta

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Evidence-Based Quality-Aware Agile software Development Process: Design and Evaluation

SSRN

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SSRN 2023年

作者： Karhapää, Pertti Behutiye, Woubshet Seppänen, Pertti Rodríguez, Pilar Oivo, Markku Franch, Xavier Martinez-Fernandez, Silverio López, Lidia Choraś, Michal Bagnato, Alessandra Aaramaa, Sanja Partanen, Jari University of Oulu Oulu90014 Finland Department of Service and Information System Engineering UPC-BarcelonaTech Barcelona08034 Spain Kaiserslautern67663 Germany ITTI Sp. z o.o Poznan61-612 Poland University of Science and Technology UTP Univerisy of Science and Technology Bydgoszcz5-796 Poland Softeam Paris75016 France Nokia Oy Oulu90620 Finland Bittium Wireless Ltd. Oulu90590 Finland University of Puget Sound United States Dep. Of Languages Computer Systems and Software Engineering Faculty of Computer Science Universidad Politécnica de Madrid Spain Dep. ESSI GESSI Research Group UPC-BarcelonaTech Barcelona08034 Spain Centro Nacional de Supercomputacíon Barcelona08034 Spain

Context Agile software development (ASD) aims to deliver high-quality software. However, software quality often gets less attention due to focus on *** an evidence-based quality-aware ASD process that organizations can use to create a more quality-aware development process and improve software *** methodUsing design science research, we collaborated with four organizations applying ASD in different contexts. Involving a total of 35 practitioners, we designed company-specific evidence-based quality-aware ASD processes, which were validated through simulated scenarios, implemented, and evaluated by the practitioners. ResultsA general ASD process model, derived from the company-specific models, was created, which shows the specific activities, frequencies, roles, and development phases where evidence can be used to improve software quality. The new process model contributed towards improved bottleneck identification, release readiness, and quality issues prioritization. We also learned that companies must be mindful of the rules that determine the use of evidence. Conclusion Integrating the use of quality related evidence into the ASD process can make it quality-aware, supporting software quality management, and enabling timely reaction through fast feedback loops. The evidence-based quality-aware ASD process is applicable in any Scrum-like ASD process. Applicability in other forms of ASD, however, requires further research. © 2023, The Authors. All rights reserved.

关键词： Quality control

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Efficacy of Cupping in the Treatment of Hypertension Disease Using Graph Colouring

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Journal of Physics: Conference Series 2019年第1期1366卷

作者： Marta a/p Elizabeth Yuhani Binti Yusof Mohd Sham Bin Mohamad Muhamad Faiz Abu Bakar Mohd Adhha Ibrahim AIMs Research Cluster Centre for Mathematical Sciences Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Kuantan Pahang Malaysia Pusat Bekam Al-Yakin Kuantan Pahang Malaysia

Cupping therapy also well known as Hijama is an ancient and holistic method for treatment variety of infirmities particularly cardiovascular diseases such as hypertension. In this method, suction mechanism used to create partial vacuum inside the cup that mobilizes the blood flow and promotes effective healing at the specific point. As practise, the selection of the points based is on patient's complaints. The aim of this study is to identify the least number yet the most effectiveness point at minimal cost to treat hypertension disease. The graph colouring method is applied, besides C# language is used to validate the result. The finding of this research indicates that the optimum number of hypertension medical cupping points is two.

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

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

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IEEE International Conference on Cognitive Informatics

作者： Witold Kinsner Haibin Zhu George Baciu Guiming Luo Fernando Rubio Jie Sui Runhe Huang Hironori Hiraishi Jun Peng Liang Chen FI2CICC Dept of ECE Univ. of Manitoba Winnipeg Canada FI2CICC Nipissing University ON Canada FI2CICC Dept. of Computing Hong Kong Polytechnic University Hong Kong FI2CICC Software Institute Tsinghua University Beijing China FI2CICC Dept. of Computer Systems &#x0026 Computation Complutense Univ. of Madrid Spain FI2CICC Aberdeen University Aberdeen UK FI2CICC Faculty of Computer and Information Sciences Hosei University Japan Faculty of Engineering Ashikaga University Tochigi Japan FI2CICC School of AI Chongqing Univ. of Science and Technology Chongqing China Dept. of Computer Science Univ. of Northern British Columbia BC Canada

ISBN: (纸本)9781665421201

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.

关键词： Knowledge engineering Intelligent vehicles Autonomous systems Conferences Cognitive informatics Cognitive robotics Cognitive systems

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