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Impact of the Intelligent Control System in Assisting Humans Regarding Complex Information Processing 4

Impact of the Intelligent Control System in Assisting Humans...

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4th international conference on Inventive Research in computing Applications, ICIRCA 2022

作者： Ambika, R. Lahane, Prashant Kumar, G. Rabbi, Fazle Maarif, Muhammad Anas Qureshi, Mohmad Kashif Bms Institute of Technology and Management Department of Ece India Mit World Peace University Faculty of Engineering India Srm Institute of Science and Technology Department of Management India Australian Computer Society Australia Institut Pesantren Kh. Abdul Chalim Pacet Mojokerto Indonesia Sanskriti University Computer Science & Engineering Professor Cum Data Scientist India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Intelligent control system is a control technique that utilises different computing approaches such as fuzzy logic, Bayesian probability, neural networks and machine learning. Information processing is change of information within a manner detectable through an observer. It describes everything that happens in universe from falling of rocks to printing text files from a digital computer system. this paper aimed to analyse the impact of intelligent control systems (ICS) to assist humans with complex information systems. In order to achieve this aim, the paper has conducted a literature review to understand other authors' perspectives. It has discussed concept of ICS and its role in assisting information systems. Additionally, the paper has analysed information processing and other information about it. In order to collect relevant data on selected research topic, paper has conducted a secondary data collection method. It has selected data from secondary resources and analysed data with help of thematic analysis. this data analysis method helped to understand data patterns on selected research topic. Based on data analysis, results found that key methods in intelligence control are fuzzy control, neural network control, and expert control and these controls offer useful and relevant programming regarding complex information systems. Additionally, intelligent control can adapt, learn and plan under a huge uncertainty as well as cope with a huge amount of information. Finally, based on robust discussion, paper has formulated an appropriate conclusion for this research. © 2022 IEEE.

关键词： Fuzzy control

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An Efficient Big data Gathering in Wireless Sensor Network using Reconfigurable Node Distribution Algorithm 4

An Efficient Big Data Gathering in Wireless Sensor Network u...

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4th international conference on Cognitive computing and Information Processing, CCIP 2022

作者： Mallikarjunaswamy, S. Basavaraju, N.M. Sharmila, N. Mahendra, H.N. Pooja, S. Deepak, B.L. JSS Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India JSS Academy of Technical Education Bengaluru India JSS Science and Technology University Mysuru India K S Insitute of Technoloy Dept of Electronics and Communication Engineering Bengaluru India R.L.Jalappa Institute of Technology Dept of Computer Science and Engineering Kodigehalli India

ISBN: (数字)9781665456487

ISBN: (纸本)9781665456487

In recent days, communication process is performed in wireless sensor networks (WSNs). the WSN is effectively reconstructed and accurately communicates the information to each corner of high-density nodes of cellular area. the main objective of this paper is to improve the data gathering of all sensor nodes and efficiently optimize the energy utilisation of each node in WSNs. the cluster head is identified in highly node distributed WSNs. the sink nodes during big data gathering are effectively utilized in WSNs with the help of Reconfigurable Node Distribution Algorithm (RNDA). the proposed algorithm procedure has been followed to address the big data gathering location in WSNs and mobilize the sink nodes in optimized location for proper communication in WSNs. the performance analysis and comparison between proposed and existing methods have been carried out with respect to energy efficiency, packet delivery ratio, packet ratio and transmission energy. the simulation result shows that the proposed method reduces energy consumption by 2 % in high density sensor node network communication process. the proposed method effectively selects the cluster head in WSNs to enhance the throughput packet delivery ratio and transmission energy. © 2022 IEEE.

关键词： Big data

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Analytic hierarchy clustering research of all cargo airlines based on safety information 4

Analytic hierarchy clustering research of all cargo airlines...

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4th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2022

作者： Liu, Nan Sun, Chuanqi Wang, Haofeng Li, Xiang Zhou, Xiuting Zheng, Ying'Er Aviation Safety Institute China Academy of Civil Aviation Science and Technology Beijing China Computing Australian National University Canberra Australia

ISBN: (数字)9781665467667

ISBN: (纸本)9781665467667

Due to the COVID-19, air passenger transport industry is sluggishm. At the same time, based on the strong demand for freight business. Airlines focus on developing cargo sector. All-cargo airlines are gradually being established. the number of registered cargo aircraft has gradually increased in recent years,and cargo transport flight hours are gradually increasing. this leads to an increase in cargo aviation security incidents. the safety information analysis of cargo aircraft needs to be solved urgently. In this paper, a hierarchical analysis and clustering research on all-cargo airlines based on China aviaiton safety information data is carried out. the results show that all-cargo airlines security incidents are divided into three categories. the paper provides security recommendations for different incident categories. © 2022 IEEE.

关键词： Cargo aircraft

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Target Recognition Method Based on Incremental Learning 4

Target Recognition Method Based on Incremental Learning

引用

4th international conference on Applied Machine Learning, ICAML 2022

作者： Qin, Weize Institute of Computing Technology Chinese Academy of Science Beijing China

ISBN: (纸本)9781665462655

the traditional batch learning method is only suitable for the solidification recognition model with a fixed number of samples. When the number of new training samples is increasing, the existing and new training samples must be reintegrated, and the recognition model must be retrained to complete the model update. the time cost, computational cost and storage resources of each update are very high, which greatly reduces the efficiency of recognition. In this paper, a new target recognition algorithm based on incremental learning is proposed. By expanding the feature extraction network and classification network, the problem of catastrophic forgetting is effectively suppressed. At the same time, the decoupling small sample incremental learning model based on prototype registration uses meta-learning method in the training process to effectively improve the efficiency of model learning on small sample data. the results are verified on CIFA100 and Tianzhi Cup challenge data sets. After two incremental learning, the recognition accuracy of the algorithm is reduced by only 21 % and 13 %, which is higher than the existing best algorithm by 4.7 % / 19.6 %. the results show that the method based on sample playback and model expansion is compatible, which can improve the accuracy of existing methods. © 2022 IEEE.

关键词： Efficiency

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Perspectives on the Impact of Artificial Intelligence & Machine Learning on Processes & Structures Engineering 4

Perspectives on the Impact of Artificial Intelligence & Mach...

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Varshney, Rajat Kishor Pratap Singh Chauhan, Sanjay Sharma, Vishnu Galgotias University Dept of Computer Science & Engineering India Galgotias College Dept of Computer Science & Engineering Gr.Noida India

ISBN: (纸本)9781665474368

In recent years, the sectors of artificial intelligence and machining learning have experienced rapid expansion, and they have become part of nearly every aspect of the technological life of societies. Over the last 5 years or fewer, thousands of publications have reported advances in a particular field. However, materials and structures engineers seem to be reluctant to take advantage of these advances. Despite recent advancements on informatics methods for materials, it is still difficult to distinguish these developments from the long-established methods of materials engineering. this undermines the clear impact of these recent developments in the field of materials, processes, and structures engineering (MPSE). On the other hand, the lack of available relevant material data and the diversity of the data types pose some challenges for the implementation of machine- learning. the snapshot presented in this overview is meant to pull the reader's attention to selected advances that we consider distinct, and to demonstrate that these technologiesare capable of having significant impacts on MPSE in the years to come. Moreover, MPSE fields have the opportunity to contribute to machine-learning tools from a physics perspective to gain an understanding of the emerging methods of machine learning. MPSE needs to make the methods more widely accessible and use them throughout the school system and start the process of transforming engineering education in order to ensure the methods will be used © 2022 IEEE.

关键词： Engineering education

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Backpropagation-Based Deep Learning Model for Privacy-Preserving of confidential data 4th

Backpropagation-Based Deep Learning Model for Privacy-Preser...

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4th international conference on data, Engineering, and Applications, IDEA 2022

作者： Soni, Mukesh Singh, Dileep Kumar Department of Computer Science and Engineering Jagran Lakecity University Bhopal India

ISBN: (纸本)9789819700363

Methods for neural network learning that rely on backpropagation are commonly used in fields including intrusion detection, bioinformatics, homeland security, and medical diagnostics. these application sectors extract trends and patterns from enormous volumes of complex information. In the application categories mentioned, protecting personal information and sensitive data is a critical concern. Most of the backpropagation neural network learning techniques now in use do not consider how to safeguard sensitive data during learning. this study suggests using a backpropagation neural network technique since it preserves privacy and works well with horizontally partitioned data. the network weight vector must be produced for the training sample set when building the neural network. Information is not made public to preserve the neural network learning model's privacy. In this study, it is suggested that the weight vector is divided among all participants, with a portion of its value going to each participant. Each layer of the neural network safely computes the final and intermediate weight vector values of the network utilizing a secure multi-party computing protocol. the learning model developed may be safely distributed across all participants, who can then use it to predict the output corresponding to their objective data. the experimental results show that the recommended method outperforms traditional non-privacy-preserving algorithms in speed and accuracy. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Memory-efficient Combination of Tensor Decomposition and Recomputation for Training CNNs 4

Memory-efficient Combination of Tensor Decomposition and Rec...

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4th international conference on Frontiers Technology of Information and Computer, ICFTIC 2022

作者： Wang, Baofang Tong, Weiqin School of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (纸本)9798350321951

Tensor decomposition and recomputation are two common strategies used to to accelerate training and save memory during the training phase of deep neural network, respectively, allowing researchers to consider larger models, batch sizes, or higher-resolution data. Tensor decomposition reduces parameters and computation by decomposing weight tensors, while recomputation trades computation time for memory. As these two strategies are independent and have their own advantages and disadvantages, it is applealing to consider the simultaneous combination of two strategies to save more memory and complement each other. We analyze existing tensor decomposition algorithms and recomputation techniques, and we propose a memory-optimized algorithm that effectively combines them, and finally evaluate the performance of the proposed algorithm on commonly used deep neural networks and computing platforms. Our experiments show that tensor decomposition can partially or even completely eliminate the extra time overhead caused by recomputation, and that together they can reduce the memory consumption of the training phase by about 41 %, while the loss of accuracy is less than 1.5%. © 2022 IEEE.

关键词： Deep neural networks

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TRIPORA: Intelligent Machine Learning Solution for Sri Lanka Touring Access and Updates 4

TRIPORA: Intelligent Machine Learning Solution for Sri Lanka...

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4th international conference on Advancements in computing, ICAC 2022

作者： Legrand, T.R. Bandara, K.M.R.A.I. Stefania Crishani, J.A.D. Uvindu, L.W.P. Amarasena, Nelum Kasthurirathna, Dharshana Department of Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

Sri Lanka is one of the top tourist destinations in the world. However, tourists face various inconveniences due to the obsolescence of facilities. there are various tools designed to solve such problems. But they are scattered in different places and users have to use different tools. the biggest issue in the tourist sector is that travelers are unable to get the most out of their tours since there may be days when a large number of people visit the same location, causing the location to become overcrowded, and preventing tourists from enjoying their visit as anticipated. there are seasons when natural disasters occur, as well as human-centered crises. Also, there are situations when travelers feel helpless because they are unable to find the best tour guide for them. We developed a cost-effective, automatic, and efficient Machine Learning-based recommendation system as a result of this research. Based on past data on tourists and data received from the SLTDA, this research can provide the best trip plan with the tour guide and provide destination news alerts on regular basis. Furthermore, in order to achieve the best accuracy through the system, unique machine learning approaches were used in this study. © 2022 IEEE.

关键词： Obsolescence

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Secure Authentication Scheme for Medical care applications based on IoT 4

Secure Authentication Scheme for Medical care applications b...

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4th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2022

作者： Rajasekaran, Arun Sekar Yuvaraj, D. Maria, Azees Gmr Institute of Technology Dept. of Ece Andhra Pradesh India Cihan University - Duhok Kurdistan Dept. of Computer Science Region Iraq

ISBN: (纸本)9781665400732

In the medical business, the Internet of Medical things (IoMT) has turned into a specialist application framework. It is utilized to accumulate and break down the physiological boundaries of patients. the vital body parameters are analyzed by the medical sensor-hubs, which are embedded in the patient's body. It would thusly detect the patient's medical data by utilizing convenient gadgets. Since patient data is so delicate to uncover without the help of a medical expert, the security and protection of medical information are turning into a difficult issue for the IoMT. Hence, an anonymous authentication protocol based on IoT is likely to be presented in this paper. this work is liked to determine the security protection issues in the IoMT. In this work, a secure and anonymous patient and medical advisor authentication scheme is proposed to guarantee secure correspondence in medical care applications. the validation of the work is evaluated in terms of computational cost with different existing schemes using the Cygwin platform. © 2022 IEEE.

关键词： Authentication

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Electroencephalogram data Analysed through the Lens of Machine Learning to Detect Signs of Epilepsy 4

Electroencephalogram Data Analysed Through the Lens of Machi...

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4th international conference on Inventive Research in computing Applications, ICIRCA 2022

作者： Rajalakshmi, J. Ranjani, S.Siva Sugitha, G. Prabanand, S.C. Sethu Institute of Technology Research Scholar Department of Computer Science and Engineering Kariyapatti India Muthayammal Engineering College Department of Computer Science and Engineering Rasipuram India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

As most epileptic seizures only happen infrequently the EEG is essential for the categorization and diagnosis of epilepsy. Empirical interpretation of a first EEG has a low sensitivity (between 29 and 55% [29-55 percent]) when applied to adult patients. During EEG epochs in which seizures are not present, valuable EEG data is buried among the signals and is thus inaccessible to any clinician who does not have specialized training in EEG analysis. In order to distinguish people with generalized epilepsy from the general population, we create a multi-variate technique employing EEG data to comprehend the functional connectivity of the brain at the sensor level. the temporal, periodic, and time-frequency domains were probed using five different measurements to investigate eight distinct connection properties. the K-Nearest Neighbor method was used to evaluate the solution, and the results were compared to two groups: the first consisted of patients with epilepsy, and the second consisted of patients who had nonepileptic episodes. Both EG and HC groups demonstrated high levels of classification accuracy (89%), although EG showed significant spatial-temporal abnormalities in the front central regions at the beta frequency band compared to HC. Classification accuracy for EG and NEAD was only approximately 79% because of the well-documented comorbidity of NEAD and epileptic episodes. this study suggests that seizure-free EEG data may be used to reliably differentiate between those with HC and those with specialized epilepsy. Although more research is needed to develop a diagnostic tool that might aid in treatment. © 2022 IEEE.

关键词： Machine learning

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