Journal articles: 'Soft computing technology'

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Werbos, Paul J. "Quantum technology to expand soft computing." Systems and Soft Computing 4 (December 2022): 200031. http://dx.doi.org/10.1016/j.sasc.2022.200031.

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Pawar, Rajeshwari H. "Soft Computing in Advanced Robotics." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 1263–66. http://dx.doi.org/10.22214/ijraset.2024.59963.

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Abstract: Advanced robotics has witnessed significant advancements in recent years, enabling robots to perform complex tasks in various domains such as manufacturing, healthcare, and space exploration. Soft computing techniques have emerged as powerful tools to address the challenges associated with uncertainty, imprecision, and complexity inherent in robotic systems. This paper presents a comprehensive review of the application of soft computing techniques in advanced robotics. The review encompasses various soft computing paradigms including fuzzy logic, neural networks, evolutionary algorithms, and swarm intelligence. The paper discusses the integration of these techniques in different aspects of robotic systems such as perception, planning, control, and learning. Furthermore, it highlights the strengths, limitations, and future directions of soft computing in advancing robotics technology.

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Sadiku, Matthew N. O., Chandra M. M. Kotteti, Abayomi Ajayi-Majebi, and Sarhan M. Musa. "Soft Computing in Decision Support Systems." International Journal of Advances in Scientific Research and Engineering 08, no. 02 (2022): 153–58. http://dx.doi.org/10.31695/ijasre.2022.8.2.21.

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Magdalena, L. "Soft computing for students and for society [Technology Review]." IEEE Computational Intelligence Magazine 4, no. 1 (February 2009): 47–50. http://dx.doi.org/10.1109/mci.2008.931074.

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Bosacchi, B. "On the role of Soft Computing in microelectronics technology." Soft Computing - A Fusion of Foundations, Methodologies and Applications 1, no. 2 (June 26, 1997): 57–60. http://dx.doi.org/10.1007/s005000050006.

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I. Jeena Jacob. "Fuzzy Logic Decision Model for Robust Risk Management in ubiquitous environment- A Review." December 2023 5, no. 4 (December 2023): 399–411. http://dx.doi.org/10.36548/jucct.2023.4.005.

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Soft computing models are invaluable resources for solving addressing real-world issues. Soft computing is the process of solving difficult computer problems with imprecise yet practical results through the application of approximate computations. The method enables the resolution of issues that might be too difficult or time-consuming to handle with the technology available today. The term ‘computational intelligence’ is often used interchangeably with soft computing. This article will discuss various soft computing strategies, with a particular focus on the fuzzy logic approach. The study delves into risk management in the real world using fuzzy logic techniques across multiple fields, providing a comprehensive review and comparing performance analyses.

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Beldjehem, Mokhtar, Lotfi A. Zadeh, Ronald Yager, and Madan Gupta. "Special Issue on Soft Computing for Modeling and Simulation." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 5 (September 20, 2009): 511. http://dx.doi.org/10.20965/jaciii.2009.p0511.

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Welcome to this special issue on Soft Computing for Modeling and Simulation of the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII). We are pleased to introduce 5 selected papers and 4 regular papers presented at NASTEC (North-American Simulation Technology Conference), which is a series of conferences initiated by Eurosis after in-depth discussions with Prof. Mokhtar Beldjehem and North-American Simulationists, Soft Computing Scientists, addressing issues regarding the interplays and synergies between Modeling, Simulation and Soft Computing. The first NASTEC 2008 was organized at Mc Gill University, Montré{e}al, Canada, which is its birth place. It has attracted Simulationists, researchers and practitioners of Soft Computing, attendees from academic, industry and government agencies in an exchange of ideas and shared experiences. The intent of the NASTEC'2008 event is to nurture the spirit of cooperation and strive to improve the quality of life in this global village through excellence in hybrid Soft Computing research and education by engineering of next-generation intelligent hybrid Soft Computing systems for Modeling, Simulation, Simulation-based and Data-driven Software Engineering, Web-centric Computing, E-learning and Virtual Reality Systems at the service and for the benefits of the humankind. Computer Simulation is being acknowledged as the “third leg” of scientific discovery and analysis, along with theory and experimentation. Simulation Technology aims at building the “software digital factory.” The fields of Modeling, Simulation, and Soft Computing in general have made significant progress; parts of them were reflected in the present Special issue. This issue was able to attract top-level and forefront research. The themes center on novel issues in connection with Modeling, Simulation, Soft Computing, Simulation-based Software Engineering, Web-centric Computing, Virtual Reality Systems, their interplays and synergies. We are grateful to a number of people without whom we would not have been able to put this special issue together. They include our NASTEC'08 IPC and JACIII reviewers for making this special issue possible; they have done an excellent job: We got 4.5 reviews per paper on the average. We are grateful to authors of selected papers who have considered JACIII as the target for their work, and even though we could not accommodate every submission in this issue, we hope that the reviews will be helpful to many people. We are also grateful to Prof. Toshio Fukuda, Nagoya University, and Prof. Kaoru Hirota, Tokyo Institute of Technology, the editors-in-chief, and the NASTEC 2008 conference staff for inviting us to guest-edit this Journal. Last, but not least, we are indebted to the staff of JACIII and Fuji Technology Press for making this a reality.

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Mahmudova, Shafagat. "MACHINE LEARNING FOR SOFTWARE." International Conference on Technics, Technologies and Education, ICTTE 2019 (2019): 251–55. http://dx.doi.org/10.15547/ictte.2019.04.061.

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The study machine learning for software based on Soft Computing technology. It analyzes Soft Computing components. Their use in software, their advantages and challenges are studied. Machine learning and its features are highlighted. The functions and features of neural networks are clarified, and recommendations were given.

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Kumar, Deepak. "Advances in Data Analytics, Soft Computing and Info-Comm Technology." Recent Advances in Computer Science and Communications 15, no. 5 (June 2022): 652. http://dx.doi.org/10.2174/266625581505220315111711.

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Liao, Xiangyun, Zhiyong Yuan, Pengfei Hu, and Qianfeng Lai. "GPU-assisted energy asynchronous diffusion parallel computing model for soft tissue deformation simulation." SIMULATION 90, no. 11 (October 13, 2014): 1199–208. http://dx.doi.org/10.1177/0037549714552708.

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Soft tissue deformation simulation is a key technology of virtual surgical simulation. In this work, we present a graphics processing unit (GPU)-assisted energy asynchronous diffusion parallel computing model which is stable and fast in processing complex models, especially concave surface models. We adopt hexahedral voxels to represent the physical model of soft tissue to improve the visual realistic quality and computing efficiency of deformation simulation. We also adopt the concept of free boundary to simulate soft tissue geometric characteristics more precisely during the deformation process and introduce asynchronous diffusion by using the mechanical energy of mass points to achieve realistic soft tissue deformation effects. In order to meet the requirement of real-time surgery simulation, we accelerate the soft tissue deformation by using OpenCL (Open Computing Language) and optimize the parallel computing process in several means. Experimental results have shown that the GPU-assisted energy asynchronous diffusion parallel computing model for soft tissue deformation simulation implements satisfactory effects on deformation in visual realistic and real-time quality.

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Herrera, Nelson, María Sinche Gonzalez, Jarkko Okkonen, and Raul Mollehuara. "Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal." Minerals 13, no. 11 (November 17, 2023): 1450. http://dx.doi.org/10.3390/min13111450.

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In the past two decades, the mining sector has increasingly embraced simulation and modelling techniques for decision-making processes. This adoption has facilitated enhanced process control and optimisation, enabling access to valuable data such as precise granulometry measurements, improved recovery rates, and the ability to forecast outcomes. Soft computing techniques, such as artificial neural networks and fuzzy algorithms, have emerged as viable alternatives to traditional statistical approaches, where the complex and non-linear nature of the mineral processing stages requires careful selection. This research examines the up-to-date use of soft computing techniques within the mining sector, with a specific emphasis on comminution, flotation, and pyrometallurgical and hydrometallurgical processes, and the selection of soft computing techniques and strategies for identifying key variables. From this, a soft computing approach is presented to enhance the monitoring and prediction accuracy for mineral waste disposal, specifically focusing on tailings and spent heap leaching spoils database treatment. However, the accessibility and quality of data are crucial for the long-term application of soft computing technology in the mining industry. Further research is needed to explore the full potential of soft computing techniques and to address specific challenges in mining and mineral processing.

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Zaslavskaya, Veronika L. "SOFT MEASUREMENTS AS AN EFFECTIVE TOOL FOR ANALYSIS AND EVALUATION OF MANAGEMENT DECISIONS MADE." SOFT MEASUREMENTS AND COMPUTING 11, no. 60 (2022): 53–64. http://dx.doi.org/10.36871/2618-9976.2022.11.005.

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In the realities of today, soft measurements and calculations are part of new information technologies, computer technology (sixth generation). The branch of soft computing represents the direction of soft measurements. It should be noted that the information technologies of the components in this branch are implemented according to the principle of unity of measurement processes (with metrological support of solutions). The consequence of this is the possibility of applying the advantages of soft computing to a new direction of soft measurements. This advantage may be the speed of processing or a variety of forms of presentation of results. The application of these advantages is aimed at improving the measurement result in the process of its implementation. The areas of application for soft computing systems are becoming wider, the computing power is increasing, and for the first time we are also able to purposefully assess the quality of the result obtained and regulate it.

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s, Sowmiyaa S., Moghana Lavanya S,, Mahendran K, and Geethalakshmi V. V. "An Insight into Fuzzy Logic Computation Technology and Its Applications in Agriculture and Meteorology." Oriental journal of computer science and technology 13, no. 0203 (January 30, 2021): 97–101. http://dx.doi.org/10.13005/ojcst13.0203.06.

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Speaking of recent advances, many computing technologies have been applied to several domains and have proved to provide more approximate and acceptable results. Fuzzy logic being one of them has been very useful in solving many real-world problems that are inherent for their uncertainty, complexity, impreciseness and a high degree of randomness. Soft computing aims to mimic human thinking and thus solve problems as a human does. The systems embedded with one or more soft computing technologies tend to make decisions quicker (reducing the processing timeframe) and more accurate in the light of uncertain and indefinite data. This paper aims at an extensive review of fuzzy logic also unraveling some of the applications of the same in the field of agricultural science and meteorology.

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Sindhwani, Manoj, Shippu Sachdeva, Krishan Arora, Taehyun Yoon, Daeseung Yoo, Gyanendra Prasad Joshi, and Woong Cho. "Soft Computing Techniques Aware Clustering-Based Routing Protocols in Vehicular Ad Hoc Networks: A Review." Applied Sciences 12, no. 15 (August 7, 2022): 7922. http://dx.doi.org/10.3390/app12157922.

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The vehicular ad hoc network is an emerging area of technology that provides intelligent transportation systems with vast advantages and applications. Frequent disconnections between the vehicular nodes due to high-velocity vehicles impact network performance. This can be addressed by efficient clustering techniques. Several recent studies have attempted to develop optimal clustering algorithms to improve network performance metrics using soft computing techniques. Although sufficient work on soft computing techniques has been carried out, it seems less commonplace to find an analysis of various algorithms’ network parameters together. This paper provides a systematic analysis of the clustering-based routing protocols used in vehicular networks that are aware of soft computing techniques. The categorization is performed according to various soft computing techniques: particle swarm optimization, k-means, neural networks, artificial bee colony, genetic algorithm, firefly algorithm, and fuzzy logic. A comparative study of soft computing strategies is also provided in the survey with a focus on their objectives, along with their strengths and limitations. This survey makes it easier for researchers to pick the required soft computing technique used in vehicular networks in order to improve metrics such as packet delivery ratio, end-to-end delay, throughput, cluster lifetime, and message overhead.

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Sabina Uzbekzade, Sabina Uzbekzade. "THE USE OF "SOFT COMPUTING" FOR THE DIFFERENTIAL DIAGNOSIS OF THE FUNCTIONAL STATE OF THE CARDIOVASCULAR SYSTEM." PIRETC-Proceeding of The International Research Education & Training Centre 27, no. 06 (August 25, 2023): 88–95. http://dx.doi.org/10.36962/piretc27062023-88.

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The application of soft computing technology for diagnosing the functional state of the cardiovascular system is considered. Soft computing technology uses fuzzy sets, fuzzy logic, fuzzy neural networks, genetic algorithms and evolutionary modeling as tools. Various methods of soft computing technology in solving various problems often complement each other when used in various combinations. This technology is focused on solving control problems with semi-structured control objects. The main informative indicators (indicator variables) characterizing the functional state of the cardiovascular system and obtained on the basis of statistical information are identified. These informative indicators include the tension index, the vegetative rhythm index, the indicator of the adequacy of regulatory processes, the tension index of regulatory systems, and also special indicators that are derivatives of classical statistical indicators: respiratory modulation index, functional arrhythmia index, cardiorespiratory synchrony index, parasympathetic control destabilization index. The quality of the classification of possible diseases is determined by indicators such as sensitivity, specificity, predictive value and diagnostic efficiency. Keywords: Neural networks, fuzzy inferences, diagnostic conclusion, confidence coefficient.

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Kumar, Deepak, Sushil Kumar, Rohit Bansal, and Parveen Singla. "A Survey to Nature Inspired Soft Computing." International Journal of Information System Modeling and Design 8, no. 2 (April 2017): 112–33. http://dx.doi.org/10.4018/ijismd.2017040107.

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This article describes how swarm intelligence (SI) and bio-inspired techniques shape in-vogue topics in the advancements of the latest algorithms. These algorithms can work on the basis of SI, using physical, chemical and biological frameworks. The authors can name these algorithms as SI-based, inspired by biology, physics and chemistry as per the basic concept behind the particular algorithm. A couple of calculations have ended up being exceptionally effective and consequently have turned out to be the mainstream devices for taking care of real-world issues. In this article, the reason for this survey is to show a moderately complete list of the considerable number of algorithms in order to boost research in these algorithms. This article discusses Ant Colony Optimization (ACO), the Cuckoo Search, the Firefly Algorithm, Particle Swarm Optimization and Genetic Algorithms in detail. For ACO a real-time problem, known as Travelling Salesman Problem, is considered while for other algorithms a min-sphere problem is considered, which is well known for comparison of swarm techniques.

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Muhammad Adnan, Rana, Xiaohui Yuan, Ozgur Kisi, Yanbin Yuan, Muhammad Tayyab, and Xiaohui Lei. "Application of soft computing models in streamflow forecasting." Proceedings of the Institution of Civil Engineers - Water Management 172, no. 3 (June 2019): 123–34. http://dx.doi.org/10.1680/jwama.16.00075.

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SEE, LINDA, and STAN OPENSHAW. "Applying soft computing approaches to river level forecasting." Hydrological Sciences Journal 44, no. 5 (October 1999): 763–78. http://dx.doi.org/10.1080/02626669909492272.

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Jang, Hyongdoo, and Erkan Topal. "A review of soft computing technology applications in several mining problems." Applied Soft Computing 22 (September 2014): 638–51. http://dx.doi.org/10.1016/j.asoc.2014.05.019.

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Kumar, Kanhaiya, Lokesh Varshney, A. Ambikapathy, Vrinda Mittal, Sachin Prakash, Prashant Chandra, and Namya Khan. "Soft computing and IoT based solar tracker." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 3 (September 1, 2021): 1880. http://dx.doi.org/10.11591/ijpeds.v12.i3.pp1880-1889.

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<p>The significance of the solar energy is to intensify the effectiveness of the Solar Panel with the use of a primordial solar tracking system. Here we propounded a solar positioning system with the use of the global positioning system (GPS) , artificial neural network (ANN) and image processing (IP) . The azimuth angle of the sun is evaluated using GPS which provide latitude, date, longitude and time. The image processing used to find sun image through which centroid of sun is calculated and finally by comparing the centroid of sun with GPS quadrate to achieve optimum tracking point. Weather conditions and situation observed through AI decision making with the help of IP algorithms. The presented advance adaptation is analyzed and established via experimental effects which might be made available on the memory of the cloud carrier for systematization. The proposed system improve power gain by 59.21% and 10.32% compare to stable system (SS) and two-axis solar following system (TASF) respectively. The reduced tracking error of IoT based Two-axis solar following system (IoT-TASF) reduces their azimuth angle error by 0.20 degree.</p>

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Tanaka, Kazuo. "Advanced Computational Intelligence in Control Theory and Applications." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 2 (April 20, 1999): 67. http://dx.doi.org/10.20965/jaciii.1999.p0067.

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We are witnessing a rapidly growing interest in the field of advanced computational intelligence, a "soft computing" technique. As Prof. Zadeh has stated, soft computing integrates fuzzy logic, neural networks, evolutionary computation, and chaos. Soft computing is the most important technology available for designing intelligent systems and control. The difficulties of fuzzy logic involve acquiring knowledge from experts and finding knowledge for unknown tasks. This is related to design problems in constructing fuzzy rules. Neural networks and genetic algorithms are attracting attention for their potential in raising the efficiency of knowledge finding and acquisition. Combining the technologies of fuzzy logic and neural networks and genetic algorithms, i.e., soft computing techniques will have a tremendous impact on the fields of intelligent systems and control design. To explain the apparent success of soft computing, we must determine the basic capabilities of different soft computing frameworks. Give the great amount of research being done in these fields, this issue addresses fundamental capabilities. This special issue is devoted to advancing computational intelligence in control theory and applications. It contains nine excellent papers dealing with advanced computational intelligence in control theory and applications such as fuzzy control and stability, mobile robot control, neural networks, gymnastic bar action, petroleum plant control, genetic programming, Petri net, and modeling and prediction of complex systems. As editor of this special issue, I believe that the excellent research results it contains provide the basis for leadership in coming research on advanced computational intelligence in control theory and applications.

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Eguavoen, Victor, and Emmanuel Nwelih. "Hybrid Soft Computing System for Student Performance Evaluation." Studia Universitatis Babeș-Bolyai Engineering 68, no. 1 (November 14, 2023): 3–17. http://dx.doi.org/10.24193/subbeng.2023.1.1.

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Education Institutions have deployed technology accelerated learning systems and innovations for effective learning outcomes. Evaluating student’s performance in these systems must align with the cognitive, affective, and psychomotor learning domains. In this research, a Hybrid soft computing system comprising of the Clustering Algorithm, Machine learning technique, and Optimization algorithm were hybridized and implemented to evaluate student academic performance using academic, social, and economic data of students. The proposed model demonstrated the best results with the lowest mean square error (MSE) and root mean square error (RMSE) values of 0.17 and 0.41, respectively. Additionally, the GANFIS model achieved values of 0.25 and 0.50, respectively, which slightly outperformed the proposed FCM-PSOANFIS model. The proposed model works better with bigger datasets, and it delivers higher predictive findings under settings that depict student learning capacities while assessing student academic achievement.

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Thakur, Gajendra Singh, Sunil Kumar Sahu, N. Kumar Swamy, Manish Gupta, Tony Jan, and Mukesh Prasad. "Review of Soft Computing Techniques in Monitoring Cardiovascular Disease in the Context of South Asian Countries." Applied Sciences 13, no. 17 (August 23, 2023): 9555. http://dx.doi.org/10.3390/app13179555.

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The term “soft computing” refers to a system that can work with varying degrees of uncertainty and approximations in real-life complex problems using various techniques such as Fuzzy Logic, Artificial Neural Networks (ANN), Machine Learning (ML), and Genetic Algorithms (GA). Owing to the low-cost and high-performance digital processors today, the use of soft computing techniques has become more prevalent. The main focus of this paper is to study the use of soft computing in the prediction and diagnosis of heart diseases, which are considered one of the major causes of fatalities in modern-day humans. The heart is a major human organ that can be affected by various conditions such as high blood pressure, diabetes, and heart failure. The main cause of heart failure is the narrowing of the blood vessels due to excess cholesterol deposits in the coronary arteries. The objective of this study is to review and compare the various soft computing techniques that are used for the prediction, diagnosis, failure, detection, identification, and classification of heart disease. In this paper, a comprehensive list of recent soft computing techniques in heart condition monitoring is reviewed and compared with an experiment with specific applications to developing countries including South Asian countries. The relevant experimental outcomes demonstrate the benefits of soft computing in medical services with a high accuracy of 99.4% from Fuzzy Logic and Convolutional Neural Networks, with comparable results from other competing state-of-the-art soft computing models.

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Thiruvalar, V. Naren, Yamini R., Manimekalai Dr.M.A.P., Dr I. Wayan Suryasa, and Sugapriya S. "Enhancing User Experiences in Ubiquitous Soft Computing Environments with Fuzzy Agent Middleware." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 14, no. 3 (September 30, 2023): 25–35. http://dx.doi.org/10.58346/jowua.2023.i3.003.

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In the Ubiquitous Computing Environment (UCE) context, the successful provision of user-required services necessitates the collaboration of various system components, encompassing hardware parts, software components, and network connections. The utilization of UCE has presented some difficulties to customers seeking services in a diverse environment, including excessive Memory Usage (MU) and prolonged Component Construction Time (CCT). To optimize the user's experience, fuzzy agent employs a non-intrusive approach in online deep-rooted learning to get insights into user behavior. Integrating the advancements mentioned above aims to enhance the connectivity between users and information technology devices by utilizing an invisible network of UCE devices, creating dynamic computational environments that can effectively meet the users' needs. This work presents a unique methodology called Fuzzy Agent Middleware to Enhance User Experiences (FAM-EUI), which aims to improve user experiences in contexts where computer technology is seamlessly incorporated into everyday activities. This research endeavors to tackle the issues associated with imprecise data and enhance user-friendly interactions by integrating fuzzy logic with intelligent agents. The results highlight the potential of FAM in enhancing user interaction within ubiquitous soft computing, leading to improved efficiency and user-centered computing systems. This study provides valuable insights into integrating soft computing and agent-based technologies to enhance ubiquitous computing paradigms.

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Marković, Dušan, Dalibor Petković, Vlastimir Nikolić, Miloš Milovančević, and Biljana Petković. "Soft computing prediction of economic growth based in science and technology factors." Physica A: Statistical Mechanics and its Applications 465 (January 2017): 217–20. http://dx.doi.org/10.1016/j.physa.2016.08.034.

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Hirota, Kaoru, Hajime Nobuhara, Kazuhiko Kawamoto, and Shin’ichi Yoshida. "Image Compression and Reconstruction based on Fuzzy Relation and Soft Computing Technology." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 1 (January 20, 2004): 72–80. http://dx.doi.org/10.20965/jaciii.2004.p0072.

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A fast image reconstruction method for Image Compression method based on Fuzzy relational equation (ICF) and soft computing is proposed. In experiments using 20 images (Standard Image DataBAse), the decrease in image reconstruction time to 1/132.02 and 1/382.29 are obtained when the compression rate is 0.0156 and 0.0625, respectively, and the proposed method outperforms the conventional one in the Peak Signal to Noise Ratio (PSNR). ICF using nonuniform coders over YUV color space is proposed in order to achieve effective compression. Linear quantization of compressed image data is introduced in order to improve the compression rate. Through experiments using 100 typical images (Corel Gallery, Arizona Directory), PSNR increases at 7.9-14.1% compared with the conventional method under the condition that compression rates are 0.0234-0.0938.

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Kesgin, Ugur, and Hasan Heperkan. "Simulation of thermodynamic systems using soft computing techniques." International Journal of Energy Research 29, no. 7 (2005): 581–611. http://dx.doi.org/10.1002/er.1095.

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Kumar, Rakesh, Narendra K. Goel, Chandranath Chatterjee, and Purna C. Nayak. "Regional Flood Frequency Analysis using Soft Computing Techniques." Water Resources Management 29, no. 6 (January 20, 2015): 1965–78. http://dx.doi.org/10.1007/s11269-015-0922-1.

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Chanda, Mriganka Mohan, Neelotpaul Banerjee, and Gautam Bandyopadhyay. "Effective Implementation of Knowledge Management Systems (KMS) in Government Schemes/Programs of Selected Sectors Using Soft Computing." International Journal of Knowledge Management 18, no. 1 (January 2022): 1–22. http://dx.doi.org/10.4018/ijkm.297608.

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Soft Computing (SC) technique consisting of several fields of Artificial Intelligence namely, Artificial Neural Networks (ANNs), Fuzzy Logic (FL), Genetic Algorithms (GA), Machine Learning (ML) and Probabilistic Reasoning (PR) is a new paradigm in computing based on a creative mix of comparatively newer computing techniques. Here, we have taken into consideration several important schemes/ programs of selected sectors pertaining to different Central Ministries/ Departments of Government of India and observed that proper Knowledge Management System (KMS) can be developed and implemented in such cases using ANN based soft computing input output model as an effective tool for the same. Further, we have analyzed how various input and output parameters (both physical and financial) associated with each such selected scheme/ program are related to each other in the light of their basic objectives and the way these can be appropriately represented by an ANN based soft computing input output model in which the expected outputs corresponding to certain inputs can be calculated/ estimated.

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Neshe, Nishat Nabila Haque, and Manwinder Singh. "Hybrid Spectrum Sensing Techniques in 5G Cognitive Radio Networks in Soft Computing: A Review." Computational Intelligence and Machine Learning 2, no. 1 (April 20, 2021): 40–49. http://dx.doi.org/10.36647/ciml/02.01.a006.

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This paper describes an updated and efficient method for Hybrid spectrum sensing in cognitive radio (CR) system utilizing soft computing paradigms. The suggested soft computing approach utilizes an artificial neural network and for learning and decision making as a solution to the problems when a new product is subjected to the CR framework, developed the ability for unlicensed cognitive users to access radio frequencies through a spectrum hole and understand its implications through mechanisms spectrum sensing. The suggested soft computing approach could then be referred to as the Neuro technique. The need for higher bandwidth is important with the rise in the number of communication devices. Usage of cognitive radio for the fifth generation 5G communication network of the next generation Consider the fact that CR technology will efficiently optimize the use of much of the unused communication spectrum bands for the future 5G of wireless network and beyond. Keyword : Hybrid spectrum sensing, 5G Cognitive Radio (CR), ANN technique.

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KOVACS, Attila Mate. "SOFT COMPUTING IN PREVENTING RANSOMWARE RELYING ON LARGER-SCALE DATA AND ANALYSIS." Strategic Impact 87, no. 2 (September 28, 2023): 66–84. http://dx.doi.org/10.53477/1842-9904-23-12.

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Ransomware attacks continue to pose a significant threat to organizations and individuals worldwide. The attackers’ ability to constantly evolve and adapt their tactics challenges traditional cybersecurity approaches to keep pace. Ransomware attacks targeting the healthcare industry accounted for 45% of all reported cyberattacks. The nature and scale of attacks and the increasing healthcare technology adoption will continue to pose ransomware attack risks. However, by collecting and analyzing large volumes of data and applying soft computing techniques, cybersecurity experts can improve their ability to detect and prevent ransomware attacks. As a result, soft computing offers options for detecting and preventing malware attacks. Using methods from the field of soft computing, such as fuzzy logic, neural networks, and genetic algorithms, makes it possible to conduct a thorough analysis of large data sets. These can yield insightful information that can help recognize and react to ransomware attacks. These techniques can also help to decrypt files that have been encrypted using ransomware.

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Chouhan, Siddharth Singh, Ajay Kaul, and Uday Pratap Singh. "Soft computing approaches for image segmentation: a survey." Multimedia Tools and Applications 77, no. 21 (May 2, 2018): 28483–537. http://dx.doi.org/10.1007/s11042-018-6005-6.

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Domingos, Roberto P., Roberto Schirru, and Aquilino Senra Martinez. "Soft computing systems applied to PWR's xenon." Progress in Nuclear Energy 46, no. 3-4 (January 2005): 297–308. http://dx.doi.org/10.1016/j.pnucene.2005.03.011.

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Virk, G. S., D. Azzi, A. E. Gegov, B. P. Haynes, and K. I. Alkadhimi. "Intelligent soft-computing based modelling of naturally ventilated buildings." International Journal of Solar Energy 22, no. 3-4 (January 2002): 131–40. http://dx.doi.org/10.1080/0142591031000091112.

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Miyamoto, Sadaaki, and Seiji Yasunobu. "Selected Papers from SCIS & ISIS 2002." Journal of Advanced Computational Intelligence and Intelligent Informatics 7, no. 2 (June 20, 2003): 71. http://dx.doi.org/10.20965/jaciii.2003.p0071.

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Much work has been done recently in soft computing, reflecting the growing, widespread interest in the emerging theory and technological development in this field. Soft computing has also been the subject of a number of new scientific conferences and symposiums, including the Joint 1st International Conference on Soft Computing and Intelligent Systems (SCIS) and 3rd International Symposium on Advanced Intelligent Systems (ISIS) at the National Institute of Advanced Industrial Science and Technology at Tsukuba on October 21–25, 2002. The first and second ISIS were held in Korea, and the joint conference has become internationally recognized. The conference at Tsukuba featured over 200 papers and discussions and information exchange by over 250 participants interested in state–of–the–art soft computing. The 20 papers in this special issue were selected from 209 of these conference presentations. In the selection process, guest editors first requested recommendation of papers to session chairs and organizers. From the resulting list, we asked if they could submit their papers. Submitted papers were reviewed as usual for this journal based on the JACIII standard, resulting in the acceptance of these 20. Most papers have been rewritten and updated, and we are proud of their high quality–a reflection of the SCIS & ISIS conference review process. Papers come from different areas: two focus on theory, including modal logic. Several study fuzzy control. Still others deal with different aspects of robotics. They also cover data analysis, image analysis, knowledge analysis, and language studies involving soft computing. This issue thus provides a concise summary of state–of–the–art soft computing methodologies. We thank the referees for their untiring efforts to complete reviews within the limited time given. We also thank Professor Kaoru Hirota and Professor Toshio Fukuda, editors–in–chief of this journal, for their kind invitation to this special issue. Professor Hirota, who is also the International Advisory Board Chair, and Dr. Takanori Shibata, the General Chair, are largely responsible for the great success of the SCIS & ISIS 2003 conference, where Guest Editor Miyamoto served the Program Chair and Yasunobu served the Special Sessions Chair. The results of their work are thus reflected in this issue. In closing, we also thank the staff at Fuji Technology Press for its on going assistance.

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Nourani, Vahid, Nazanin Behfar, Dominika Dabrowska, and Yongqiang Zhang. "The Applications of Soft Computing Methods for Seepage Modeling: A Review." Water 13, no. 23 (December 1, 2021): 3384. http://dx.doi.org/10.3390/w13233384.

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In recent times, significant research has been carried out into developing and applying soft computing techniques for modeling hydro-climatic processes such as seepage modeling. It is necessary to properly model seepage, which creates groundwater sources, to ensure adequate management of scarce water resources. On the other hand, excessive seepage can threaten the stability of earthfill dams and infrastructures. Furthermore, it could result in severe soil erosion and consequently cause environmental damage. Considering the complex and nonlinear nature of the seepage process, employing soft computing techniques, especially applying pre-post processing techniques as hybrid methods, such as wavelet analysis, could be appropriate to enhance modeling efficiency. This review paper summarizes standard soft computing techniques and reviews their seepage modeling and simulation applications in the last two decades. Accordingly, 48 research papers from 2002 to 2021 were reviewed. According to the reviewed papers, it could be understood that regardless of some limitations, soft computing techniques could simulate the seepage successfully either through groundwater or earthfill dam and hydraulic structures. Moreover, some suggestions for future research are presented. This review was conducted employing preferred reporting items for systematic reviews and meta-analyses (PRISMA) method.

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Cios, K. J., G. Y. Baaklini, and A. Vary. "Soft Computing in Design and Manufacturing of Advanced Materials." Journal of Engineering for Gas Turbines and Power 117, no. 1 (January 1, 1995): 161–65. http://dx.doi.org/10.1115/1.2812766.

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The goal of this paper is to show the potential of fuzzy sets and neural networks, often referred to as soft computing, for aiding in all aspects of manufacturing of advanced materials like ceramics. In design and manufacturing of advanced materials it is desirable to find which of the many processing variables contribute most to the desired properties of the material. There is also interest in real-time quality control of parameters that govern material properties during processing stages. This paper briefly introduces the concepts of fuzzy sets and neural networks and shows how they can be used in the design and manufacturing processes. These two computational methods are alternatives to other methods such as the Taguchi method. The two methods are demonstrated by using data collected at NASA Lewis Research Center. Future research directions are also discussed.

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Hariri-Ardebili, Mohammad Amin, Fernando Salazar, Farhad Pourkamali-Anaraki, Guido Mazzà, and Juan Mata. "Soft Computing and Machine Learning in Dam Engineering." Water 15, no. 5 (February 27, 2023): 917. http://dx.doi.org/10.3390/w15050917.

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Ulyanov, Sergey Viktorovich, Alla Aleksandrovna Mamaeva, and Andrey Vladimirovich Shevchenko. "The cognitive technology of intelligent control: soft computing optimizer and deep machine learning." Fuzzy Systems and Soft Computing, no. 2 (December 28, 2018): 166–82. http://dx.doi.org/10.26456/fssc44.

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Onoda, Takashi. "Preface to the Special Issue on ^|^ldquo;Advanced Technology in Soft Computing^|^rdquo;." IEEJ Transactions on Electronics, Information and Systems 130, no. 1 (2010): 1. http://dx.doi.org/10.1541/ieejeiss.130.1.

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., Ravi Sharma. "SC-IDT: SOFT COMPUTING BASED INTRUSION DETECTION TECHNOLOGY IN SMART HOME SECURITY SYSTEM." International Journal of Research in Engineering and Technology 03, no. 07 (July 25, 2014): 230–36. http://dx.doi.org/10.15623/ijret.2014.0307039.

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Fosci, Paolo, and Giuseppe Psaila. "Soft Integration of Geo-Tagged Data Sets in J-CO-QL+." ISPRS International Journal of Geo-Information 11, no. 9 (September 13, 2022): 484. http://dx.doi.org/10.3390/ijgi11090484.

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The possibility offered by the current technology to collect and store data sets regarding public places located on the Earth globe is posing new challenges, as far as the integration of these data sets is concerned. Analysts usually need to perform such an integration from scratch, without performing complex and long preprocessing or data-cleaning tasks, as well as without performing training activities that require tedious and long labeling of data; furthermore, analysts now have to deal with the popular JSON format and with data sets stored within JSON document stores. This paper demonstrates that a methodology based on soft integration (i.e., data integration performed through soft computing and fuzzy sets) can now be effectively applied from scratch, through the J-CO Framework, which is a stand-alone tool devised to process JSON data sets stored within JSON document stores, possibly by performing soft querying on data sets. Specifically, the paper provides the following contributions: (1) It presents a soft-computing technique for integrating data sets describing public places, without any preliminary pre-processing, cleaning and training, which can be applied from scratch; (2) it presents current capabilities for soft integration of JSON data sets, provided by the J-CO Framework; (3) it demonstrates the effectiveness of the soft integration technique; (4) it shows how a stand-alone tool able to support soft computing (as the J-CO Framework) can be effective and efficient in performing data-integration tasks from scratch.

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Cipriano, A. "Computationally Intelligent Hybrid Systems: The Fusion of Soft Computing and Hard Computing- [Book review." IEEE Power and Energy Magazine 3, no. 5 (September 2005): 64–65. http://dx.doi.org/10.1109/mpae.2005.1507028.

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Niu, Cao, and Dake Liu. "Detector Processor for a 5G Base Station." Sensors 22, no. 20 (October 12, 2022): 7731. http://dx.doi.org/10.3390/s22207731.

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Nonlinear soft bit detection is essential for the uplink receivers of 5G base stations, especially for users around the cell edge. However, its throughput and computing complexity are always challenges for both research and industry. A low-cost and low-power parallel implementation of a soft-output detector based on sorted QR decomposition (SQRD) and the K-best breadth-first search algorithm is thus proposed to reduce computational complexity and latency. In addition, to save area and reduce latency, two improvement methods are used: (1) reduce the computing cost by saturating and truncating large values during PED computing and (2) reduce the sorting cost by using the binary bit sorting method for a reduced sample set with finite accuracy. Furthermore, a pipelined VLSI architecture is designed using 28-nm digital CMOS technology offered by Semiconductor Manufacturing International Corporation (SMIC). It can achieve a peak throughput of 6400 Mbps while consuming 153 K gates (including all flip-flops) for SMIC’s 28-nm technology and running at 800 MHz, a 32% cost reduction compared with the published reference design.

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Karthik, S., and N. Satish. "Application and Recent Trends in Implementation of Internet of Things: A comprehensive review." YMER Digital 20, no. 12 (December 22, 2021): 533–44. http://dx.doi.org/10.37896/ymer20.12/50.

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Internet of Things (IoT) is an evolving technology in the current era with a combination of diverse computational technologies, objects, animals and human. The objects in the IoT framework transmit data among themselves and they are assigned with unique numbers for identification. The communication among the network is established by identification system and functions without any centralized system. Advancement in the sensor network has made automation in numerous field and integration of soft computing technology in the IoT system has made effective decision making. The objects resides in the IoT system acts intelligent and perform the actions intelligently. The IoT based technology enhances daily life of humans via connected devices and makes living things context-aware. The information collected from sensors will be processed with the computational algorithms and effective predictions are accomplished. In this article, recent applications and soft computing algorithms are reviewed. In addition to that numerous applications based on IoT is also discussed in this article.

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Alwendi, Al Wendi, and Khairunnisa Samosir. "PENGEMBANGAN DAN IMPLEMENTASI METODE FUZZY MAMDANI UNTUK PENILAIAN KINERJA PENELITIAN DOSEN." Jurnal Teknik Informasi dan Komputer (Tekinkom) 5, no. 2 (December 31, 2022): 333. http://dx.doi.org/10.37600/tekinkom.v5i2.533.

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Today, information technology, especially soft computing technology is growing rapidly. One of the soft computing technologies that has been widely developed is fuzzy logic. This is because it can be used to measure various phenomena that are unclear, obscured or obscured. One of the research themes that uses fuzzy logic is the assessment system in research. Research [Graha Nusantara Padangsidimpuan Data Simlitabmas Still in the Leadership category for promotion to the Madya Faculty of UGN Padangsidimpuan is challenged to develop, dedicate and apply the knowledge needed in research. The goal according to this research is to apply fuzzy reasoning with the Mamdani method for lecturer research activities at the University of Graha Nusantara Padangsidimpuan. This research uses Mamdani fuzzy logic. The Mamdani fuzzy method is a way to convert the input space to the output space.

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Maddineni, Bhavyasri. "Analysis of Beam Forming Antenna using Soft-Computing Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 14, 2021): 661–65. http://dx.doi.org/10.22214/ijraset.2021.35050.

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Antenna Beam forming is a technology or a technique that is finding increasing use in systems of cellular telecommunication, especially 5G, as well as many other wireless systems. Beam forming refers to the formation of a beam of energy from a set of phased arrays. With the use of phased arrays, it is possible to control the direction and shape of the beam from multiple antennas, based on the spacing between antennas and the phase of signal from each antenna element in the array. Beam forming focuses a wireless signal towards a specific receiving device, rather than having the signal spread in all directions from the broadcast antenna. By focusing signal in specific direction, the beam forming technique allows delivery of higher signal quality to the receiver, which would result in faster information transfer with fewer errors and without the need to boost the broadcast power. Beam forming technique is also used to reduce the interference of signals. Beam forming is majorly involved with computing resources which requires high time and power resources. Parameters that would be analyzed during this project are Half Power Beam width (HPBW), First Null Beam width (FNBW), Gain, Voltage Standing Wave Ratio, Front to Back Power Ratio, Side Lobe Levels.

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48

García, Silvia. "Soft Schemes for Earthquake-Geotechnical Dilemmas." International Journal of Geophysics 2013 (2013): 1–34. http://dx.doi.org/10.1155/2013/986202.

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Models of real systems are of fundamental importance in virtually all disciplines because they can be useful for gaining a better understanding of the organism. Models make it possible to predict or simulate a system’s behavior; in earthquake geotechnical engineering, they are required for the design of new constructions and for the analysis of those that exist. Since the quality of the model typically determines an upper bound on the quality of the final problem solution, modeling is often the bottleneck in the development of the whole system. As a consequence, a strong demand for advanced modeling and identification schemes arises. During the past years, soft computing techniques have been used for developing unconventional procedures to study earthquake geotechnical problems. Considering the strengths and weaknesses of the algorithms, in this work a criterion to leverage the best features to develop efficient hybrid models is presented. Via the development of schemes for integrating data-driven and theoretical procedures, the soft computing tools are presented as reliable earthquake geotechnical models. This assertion is buttressed using a broad history of seismic events and monitored responses in complicated soils systems. Combining the versatility of fuzzy logic to represent qualitative knowledge, the data-driven efficiency of neural networks to provide fine-tuned adjustments via local search, and the ability of genetic algorithms to perform efficient coarse-granule global search, the earthquake geotechnical problems are observed, analyzed, and solved under a holistic approach.

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Yao, Ping. "Spatial Expression of Multifaceted Soft Decoration Elements: Application of 3D Reconstruction Algorithm in Soft Decoration and Furnishing Design of Office Space." Journal of Sensors 2022 (August 28, 2022): 1–11. http://dx.doi.org/10.1155/2022/5345293.

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In China’s modern market economy under the rapid development of the general situation, we work more and more problems, and work pressure is also increasing. The so-called office space refers to the space layout, style, and the physical and psychological division of the space. Office space must take into account many factors, involving technology, technology, humanities, aesthetics, and other elements, while the office space is the space where people work and relax. In recent years, as people’s requirements for the work environment are increasingly high, therefore, the design of the office space is also more and more attention to people. The concept of soft furnishing design into the work space will help improve the overall corporate and office space design of cultural taste which is one of the main methods to show the quality and human connotation of the enterprise. The three-dimensional reconstruction refers to the creation of a mathematical model suitable for computer display and processing of three-dimensional space objects. It is an important basic tool for data processing, computing, and researching the performance of mathematical models in the computer environment, which can be applied in various fields such as autonomous navigation of mobile robots, aviation and remote sensing computing, industrial monitoring information system, medical imaging, and virtual reality. The 3D environment reconstruction technology has become one of the popular research areas in computer vision and increasingly attracts the attention of design practitioners. This paper takes the 3D environment reconstruction technology of office space soft decoration design as the basis and discusses the important elements and modeling ideas in soft decoration design, which adds to the interior design of office space, and uses Kinect to obtain the depth data in the 3D environment, so as to complete the realistic 3D reproduction of the interior environment based on computer vision technology.

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Sihag, Parveen, Balraj Singh, Alireza Sepah Vand, and Vahid Mehdipour. "Modeling the infiltration process with soft computing techniques." ISH Journal of Hydraulic Engineering 26, no. 2 (May 7, 2018): 138–52. http://dx.doi.org/10.1080/09715010.2018.1464408.

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Journal articles on the topic 'Soft computing technology'

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