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A Survey Report on recent Progresses in Nearest Neighbor Realization of Quantum Circuits 1

4th international conference on soft computing: Theories and Applications, SoCTA 2019

作者： Bhattacharjee, Anirban Bandyopadhyay, Chandan Mondal, Bappaditya Rahaman, Hafizur Department of Information Technology Indian Institute of Engineering Science and Technology HowrahWest Bengal711103 India

ISBN: (数字)9789811540325

ISBN: (纸本)9789811540318

The thrive of attending very fast computation has bred the ideas for new computing paradigms like optical computing, quantum computing, and in the last few years, quantum computing has left an impressive footprint before the design industry. Even companies like IBM, Microsoft are claiming the initiation of physical implementation of quantum circuits in on-chip units. But the successful implementation of this circuit needs to satisfy several design constraints and one such constraint is Nearest Neighbor (NN) enforcement. To introduce the reader with the progress made in NN-based implementation of quantum circuits, here, in this survey paper, we have tried to include some of the peer-reviewed works in our content. While describing the works, we have added necessary examples so that it becomes easy to follow for a reader. To this extent, we also have introduced the NN logic and its related cost functions in our survey report. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Surveys

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recent advances in soft computing and its applications

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2019年第5期31卷 699-700页

作者： Yadav, Anupam Yadav, Neha Kim, Joong Hoon Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Math Jalandhar Punjab India Natl Inst Technol Hamirpur Dept Math Hamirpur India Korea Univ Sch Civil Environm & Architectural Engn Seoul South Korea

We have a great pleasure to presenting a special issue of the 4 SP th sp international conference on Harmony Search, soft computing and Applications (ICHSA 2018) organised by BML Munjal University, Gurugram, India during 7 February 2019 to 9 February 2019. The 2018 edition of ICHSA attracts total 220 research article submissions spanning over numerous active and emerging areas of the soft computing, evolutionary algorithms, and swarm intelligence techniques along with various real life problems. The first article entitled 'Stability and iterative convergence of water cycle algorithm for computationally expensive and combinatorial Internet shopping optimization problems' is the combination of the theoretical as well as experimental work on Water Cycle Algorithm. [Extracted from the article]

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recent Development of AI and IoT in the field of Agriculture Industries: A Review 4th

Recent Development of AI and IoT in the field of Agricultur...

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4th international conference on soft computing: Theories and Applications, SoCTA 2019

作者： Kulkarni, Amith A. Dhanush, P. Chethan, B.S. Thammegowda, C.S. Shrivastava, Prashant Kumar Department of Mechanical Engineering Vidyavardhaka College of Engineering MysuruKarnataka India

ISBN: (纸本)9789811540318

In this, paper presents the new technology which in turn increases the area of application for the technology, amongst that, one of them is the agricultural application such as the adding artificial intelligence (AI) and Internet of things (IoT) in the machinery is to make it a smart device which will be capable of making decision-making capacity based on the past experiences and learning, it is the system that includes IoT (Internet of things) where automated machine learning of the process can be done, and the AI involves many logics and methods for the problem-solving process. This technology is used for the application of agricultural system, and it overcomes the various problems in the agriculture such as the crop disease infestations, lack of storage management, pesticide control, weed management, and lack of irrigation and drainage facilities, and the technology uses wireless networks for the monitoring and controlling. The main aim of this study is to explore the present and future aspects of the AI and IoT in the agriculture industries that make the agriculture systems easy, effective, and minimize the human efforts. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Weed control

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Modeling of Information Diffusion in Sina Weibo Based on Random Forest Classifier and SIR Model 15th

Modeling of Information Diffusion in Sina Weibo Based on Ran...

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15th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th international conference on Harmony Search, soft computing and Applications, ICHSA 2019

作者： Jianyi, Zhang Ping, He Tsang, Ken K. T. Yuhui, Deng Beijing Normal University-Hong Kong Baptist University United International College ZhuhaiGuangdong Province China

ISBN: (纸本)9783030324551

recent developments in information diffusion model for social network have not taken into account its topological structures. Characteristics such as the degree of connections and clustering of nodes in a network are known to influence the speed of information propagation. Yet, existing models (such as SIR with an average probability to repost received message) are not sophisticate enough to reflect the fine-grain characteristics. Differences among nodes are often overlooked, leading to inaccurate description of the information dissemination process. In this work, a new approach to predict the information diffusion probability in social network is studied. We combine the Random Forest classification and the SIR model together to analyze the dissemination of information in Weibo. Python crawlers are employed to obtain a total of 316,329 microblogs concerning major news events in 2018, together with related features of nodes from Sina Weibo. The unbalanced positive and negative repost behavior together with 15 features that characterize the nodes and edges data are rebalanced by SMOTE resampling, then used to train a Random Forest classifier to predict individual user’s forwarding behavior. For comparison, we find the performance of the Random Forest classifier, judging from the AUC of receiver operating characteristic (ROC) curve, is higher than a comparable SVM model. Finally, a Susceptible Infected Recovered (SIR) information propagation model with the forwarding rates obtained from the Random Forest classifier as input parameter is used to simulate the information dissemination process of Weibo. The predicted time behaviors of the Susceptible, Infected, and Recovered populations are in good agreement with real-life data obtained from Sina Weibo. © Springer Nature Switzerland AG, 2020.

关键词： Information dissemination

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Prediction Market Index by Combining Financial Time-Series Forecasting and Sentiment Analysis Using soft computing 15th

Prediction Market Index by Combining Financial Time-Series F...

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15th international conference on Distributed computing and Artificial Intelligence (DCAI)

作者： Saini, Dinesh Kumar Zia, Kashif Abusham, Eimad Sohar Univ FCIT Sohar Oman

ISBN: (纸本)9783319946498;9783319946481

In recent years, a lot of research is focusing on predicting real-world outcomes using Social networks data (for example, Twitter Data). Sentiment Analysis of the twitter data thus has become one of the key aspects of making predictions involving human sentiments. Stock market movements are very sensitive and it affects investment of the investors because of this prediction is the main interest of the researchers. soft computing approaches and nature-inspired computing has a lot of potential in predicting the market movement. In this paper, soft computing techniques are used to predict market trends using sentiments extracted from market data. The results indicate that by selecting suitable neural networks architecture and selecting suitable regression coefficients can improve the overall accuracy and correlation of the predictions. Stock market information people use for investment decisions. Forecasting must be accurate otherwise it will not be effective in the decision. There are techniques like trend based classification, adaptive indicators selection and market trading signals are used in forecasting.

关键词： Prediction Market index Financial Time series Sentiment Analysis soft computing

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MODELLING SAMPLE INFORMATIVENESS FOR DEEP AFFECTIVE computing 44

MODELLING SAMPLE INFORMATIVENESS FOR DEEP AFFECTIVE COMPUTIN...

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44th IEEE international conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Rizos, Georgios Schuller, Bjoern Imperial Coll London Grp Language Audio & Mus London England Univ Augsburg ZD B Chair Embedded Intelligence Hlth Care & Well Augsburg Germany

ISBN: (纸本)9781479981311

Using data with high quality annotation is crucial in emotion recognition applications, especially because the task is subjective and the raters may exhibit disagreement with respect to each sample. In this paper, we propose a meta-learning methodology that can reason about the training data and detect potentially less informative instances in order to reduce their impact in the training process. The way we inform the meta-learner on the importance of each sample is by utilising recent advances in uncertainty modelling with Bayesian neural networks that can decompose predictive uncertainty into: a) model uncertainty that is due to a lack of observations and b) label uncertainty that is due to inherent randomness in the data labelling, which we adapt for affective computing. Our proposed method for soft data selection exhibits a 6% absolute improvement in Concordance Correlation Coefficient with respect to the baseline in a two-dimensional continuous affect recognition task.

关键词： soft data selection annotation quality meta-learning Bayesian neural networks affect recognition

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Energy Efficiency in Load Balancing of Nodes Using soft computing Approach in WBAN 4th

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4th international conference on Harmony Search, soft computing and Applications (ICHSA)

作者： Rakhee Srinivas, M. B. VNRVJIET Dept Comp Sci Hyderabad Telangana India BML Munjal Univ Sch Engn & Technol Gurgaon Haryana India

ISBN: (纸本)9789811307614;9789811307607

Wireless Body Area Network (WBAN) has become very prominent in recent years for patient monitoring systems which offer the flexibility for mobility of patients and the medical staff in indoor hospital environment. A large number of patients monitoring at hospital, includes more data collection of various Body Area Network Coordinator (BANC) at different data rates transmitting to base station for further diagnosis. Traffic generated at base station due to large number of packets transmitted by Medical Device Coordinators (MDC) makes the network congestion and more energy consumption of the nodes present close to the base station. We propose soft computing approach, i.e., Ant Colony based energy-efficient algorithm using cluster based for monitoring the data packets while balancing the load at each and every intermediate node by using Ant Colony probabilistic function for routing the data from source to the base station. Our proposed algorithm helps to ensure maximum network lifetime by using the modified cluster head rotation process during route construction. In the current work, we implemented monitoring of the patients using Ant colony based and done experimental result on OMNeT++ to prove the proposed method can find better results than conventional methods.

关键词： WBAN BANC Ant Colony OMNeT plus

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A soft computing methodology for estimation and forecasting of daily global solar radiation (DGSR)

A soft computing methodology for estimation and forecasting ...

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international conference on computing, Power and Communication Technologies, GUCON 2018

作者： Christy Martina, J. Amudha, T. Bharathiar University Coimbatore India

ISBN: (纸本)9789811322532

Energy is one of the most crucial building blocks of economic development. The energy sector in India has a rapid growth in recent years. The factors that took the nation to a very acute energy crisis are increase in population, transportation, urbanization, industrialization, high standard of living and fast depleting fossil fuels. Energy demand problems have increased all over the world, and renewable energy sources are more crucial to solve these problems. Solar energy is a stepping stone to satisfy the growing energy demands in India and across the globe. In this research work, Coimbatore location was considered for daily global solar radiation (DGSR) analysis and the meteorological parameters used for this research are minimum air temperature, global solar radiation, maximum air temperature, sunshine hours, wind speed, mean air temperature, extraterrestrial radiation, average atmospheric pressure, average precipitation and relative humidity. Statistical models and artificial intelligence computational technique with ANFIS, i.e. adaptive neuro-fuzzy inference system, were used for forecasting and estimation of DGSR. The results of ANFIS model were found to be the best fit for forecasting and estimation of DGSR in any region. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Forecasting

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A Reliable Novel Framework of User-Oriented software Engineering

A Reliable Novel Framework of User-Oriented Software Enginee...

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international conference on recent Advancement in Computer, Communication and Computational Sciences, RACCCS 2018

作者： Saini, Gurpreet Singh Dubey, Sanjay Kumar Bharti, Sunil Kumar Amity University NoidaUttar Pradesh India Central University of Haryana Mahendergarh India

ISBN: (纸本)9789811359330

The reliability is one of the prominent factors for quality determination in any of the projects conducted. It has become a prime focus for the software developers and the respective teams to keep reliability as a goal of each completed module. There are some appropriate models that successfully produce intended results as well as maintain reliability in terms of functional and non-functional features. However, while achieving such results, they have surpassed the project cost estimations and even the development time. Still, almost 80% of software projects fail to complete or either miss their deadlines. Reliability is one great need of present and in this paper, we present a reliable framework for user-oriented software engineering which is based upon a hybrid approach developed with the help of fuzzy logic and the dynamic networks. This will result in reliable, fast and an early product out of the software development life cycle. © 2019, Springer Nature Singapore Pte Ltd.

关键词： soft computing

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Distinguishing Two Different Mental States of Human Thought Using soft computing Approaches

Distinguishing Two Different Mental States of Human Thought ...

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international conference on Machine Intelligence and Signal Processing (MISP)

作者： Gupta, Akshansh Kumar, Dhirendra Chakraborti, Anirban Singh, Vinod Kumar Jawaharlal Nehru Univ Sch Computat & Integrat Sci New Delhi India AIM & ACT Banasthali Vidyapith Niwai Rajasthan India

ISBN: (纸本)9789811309236;9789811309229

Electroencephalograph (EEG) is useful modality nowadays which is utilized to capture cognitive activities in the form of a signal representing the potential for a given period. Brain-Computer Interface (BCI) systems are one of the practical application of EEG signal. Response to mental task is a well-known type of BCI systems which augments the life of disabled persons to communicate their core needs to machines that can able to distinguish among mental states corresponding to thought responses to the EEG. The success of classification of these mental tasks depends on the pertinent set formation of features (analysis, extraction, and selection) of the EEG signals for the classification process. In the recent past, a filter-based heuristic technique, Empirical Mode Decomposition (EMD), is employed to analyze EEG signal. EMD is a mathematical technique which is suitable to analyze a nonstationary and nonlinear signal such as EEG. In this work, three-stage feature set formation from EEG signal for building classification model is suggested to distinguish different mental states. In the first stage, the signal is broken into a number of oscillatory functions through EMD algorithm. The second stage involves compact representation in terms of eight different statistics (features) obtained from each oscillatory function. It has also observed that not all features are relevant, therefore, there is need to select most relevant features from the pool of the formed features which is carried out in the third stage. Four well-known univariate feature selection algorithms are investigated in combination with EMD algorithm for forming the feature vectors for further classification. Classification is carried out with help of learning the support vector machine (SVM) classification model. Experimental result on a publicly available dataset shows the superior performance of the proposed approach.

关键词： Empirical mode decomposition

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