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international conference on ICT in Business Industry & Government (ICTBIG)

作者： S. Rathika Deepak Minhas Spoorthi B Santosh Kumar M. Vigenesh Prateek Garg Department of MBA Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Engineering and Technology Electrical and Electronics Engineering JAIN (Deemed to be University) Bangalore Karnataka India AIDS Vishwakarma Institute of Technology Pune India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

the advancement of information technology, the proliferation of monitoring networks, and the automated detection of anomalous behavior in surveillance video are all becoming more crucial for the purpose of public safety and smart city development. Context modeling is the foundation upon which the new methods for recognizing moving objects are built. Consequently, these systems are vulnerable to noise and background movement. It is a challenging endeavor to keep track of individuals in crowded scenarios because of the variations in movement and appearance that are brought about by the vast number of people present in the picture. An efficient Kernel Support Vector Machine is constructed for the purpose of anomaly identification in congested settings by making use of spatio-temporal movement pattern models. this is done to find solutions to these challenges. At the outset, the movie is divided into frames by making use of the threshold value. Protracted Kalman Filters are used to segment the motions of the objects to enhance the accuracy of the categorization. the textural characteristics are used to determine which item is in the front and which is in the background. To track objects in an effective manner, enhanced vector quantization is used. To categorize the abnormalities, the Kernel Support Vector Machine is used.

关键词： Support vector machines Target tracking Social networking (online) Smart cities Vector quantization Surveillance Hidden Markov models Vectors Kalman filters Kernel

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Research on GMM-Based Approach pattern recognition and Quantitative Analysis Algorithm for Flight in Airport Terminal Areas

Research on GMM-Based Approach Pattern Recognition and Quant...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), international conference on IEEE international conference on

作者： Yuxin Zhang Hongtao Zhang Chen Zhang Jingru Han China Academy of Civil Aviation Science and Technology Beijing China Engineering and Technical Research Center of Civil Aviation Safety Analysis and Prevention of Beijing Beijing China Chengdu Airborne Equipment Center of Airworthiness Certification Center CAAC

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

With the rapid growth in demand for air transportation, the efficiency evaluation of aircraft approaches in airport terminal areas has become an increasingly critical focus. this study aims to develop an efficiency evaluation algorithm for aircraft approaches in airport terminal areas based on flight big data. Initially, the study constructs a multidimensional evaluation index system that includes fuel efficiency, time efficiency, and distance efficiency using Quick Access Recorder (QAR) data. Based on this index system, a detailed analysis of Boeing 777 flights at three typical international airports—Beijing Capital, Guangzhou Baiyun, and London Heathrow—is conducted. Subsequently, a hierarchical evaluation model and a quantitative scoring algorithm based on the Gaussian Mixture Model (GMM) are innovatively proposed. this method demonstrates advantages in capturing the diversity and complexity of aircraft approach patterns and has been validated with approach phase data for Boeing 737 flights at several international and domestic airports. the findings reveal that, despite having a higher overall fuel consumption, Heathrow Airport exhibits higher efficiency in descent profile management in the Boeing 777 analysis. In contrast, Beijing Capital Airport, with lower total fuel consumption, shows room for optimization in its descent profile due to longer level flight phases. the application of the GMM model further confirms this finding and provides a quantitative basis for evaluating efficiency across different airports. this study offers new perspectives and methodologies for evaluating and optimizing operational efficiency in airport terminal areas, with significant practical implications for improving air traffic management.

关键词： Heating systems Atmospheric modeling Evaluation models Big Data Airports Complexity theory Fuels Air traffic control Indexes Aircraft

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Real Time Fake news Detection Web App Enhanced by Machine Learning Algorithms

Real Time Fake news Detection Web App Enhanced by Machine Le...

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Intelligent Technologies (CONIT), international conference on

作者： V Gunasekaran D Karthikeyan School of Information and Technology Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

In this era, the pervasive proliferation of fabricated news has come to light as a formidable adversary, corroding the credibility of journalistic sources, sowing confusion, and distorting public discourse. To counteract this escalating predicament, this research introduces a pioneering and comprehensive solution-an interactive web application engineered to detect and mitigate the impact of fabricated news. through the strategic integration of sophisticated machine learning algorithms, this platform not only identifies spurious news content but also operates in real time, enabling swift responses to the proclamation of misinformation. the key aim of this study is to elevate the precision and efficiency of fabricated news identification. this is achieved through a meticulously devised amalgamation of advanced natural language processing (NLP) techniques, meticulous feature engineering strategies, and the robust framework of ensemble learning methods. the fusion of these techniques equips the system with an arsenal of tools to scrutinize news story, disentangling the intricate web of linguistic nuances that often obscure deceptive content. the real-time facet of the application is pivotal, ensuring that the detection process transpires in sync with the constant influx of news story across digital platforms. this immediacy minimizes the potential amplification of false narratives, thereby curbing the negative impact of fabricated news proclamation. the heart of the system lies in its fabricated news detection capability, fortified by the intricacies of modern machine learning. By training on vast repositories of verified news data and contrasting it with known instances of false information, the algorithms learn to recognize patterns that distinguish authentic news from falsified narratives. Accessed through standard web browsers, the web application interface offers an intuitive and accessible medium for users to engage with the detection process.

关键词： Training Heart Machine learning algorithms Linguistics Real-time systems Natural language processing pattern recognition

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Advanced Seizure Prediction System for Wearable Health Management Devices using KNN Algorithm

Advanced Seizure Prediction System for Wearable Health Manag...

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： M. Raja Suguna Nithisha J A. Ramesh Babu M.H Ananda R. Venkatesan N. Malathi RMK College of Engineering and Technology Chennai Tamil Nadu India SRM Institute of Science and Technology Chennai Tamil Nadu India Vidya Vikas Institute of Engineering and Technology Mysore Karnataka India REVA University Bangalore Karnataka India Muthayammal Engineering College Namakkal Tamil Nadu India Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

the K-Nearest Neighbors (KNN) algorithm is used to provide a novel approach to predicting seizures in wearable health monitoring devices. Physiological signals, such as EEG and ECG, are used in the preprocessing and analysis of real-time data performed on a Raspberry Pi platform by the system. After being trained on a large dataset that comprised normal and seizure states, the KNN model demonstrates good classification accuracy. the wearable equipment, intended to be unnoticeable, performs continuous analysis of physiological data and triggers alerts upon recognizing patterns that point to the possibility of an oncoming seizure. this cutting-edge gadget has the ability to identify seizures in their earliest stages, allowing for timely medical attention and intervention to be provided to patients. It is easily incorporated into one’s routine and activities. the research highlights the importance of machine learning in healthcare wearables and the efficacy of the KNN algorithm for predictive analytics in neurological illnesses. It is also shown in the research that machine learning is relevant to healthcare wearables. Because of the study’s emphasis on neurological disorders, this result is significant.

关键词： Machine learning algorithms Accuracy Medical services Machine learning Prediction algorithms Real-time systems pattern recognition

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Load Shedding Automation System on Smart Meter Using Fuzzy Logic Method

Load Shedding Automation System on Smart Meter Using Fuzzy L...

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international conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Imam Wahyudi Farid Ciptian Weried Priananda Divan Andika Yuhansyah Siska Arifiani Ridho Rahman Hariadi Ardyono Priyadi Mauridhi Hery Purnomo Dept. Electrical Automation Engineering Dept. Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. Electrical Automation Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. Informatics Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9781665499705

the need for electrical energy for the household sector has become a basic need. However, there are still people who complain about expensive electricity bills. In addition, the use of electricity is not recommended to exceed the power capacity which can cause a short circuit. therefore, a tool is needed, namely a Smart Meter that can monitor the use of electrical energy and can perform load shedding automatically to reduce the occurrence of short circuits. the load shedding automation system uses one of the methods of artificial intelligence technology, namely by using Mamdani's Fuzzy Logic so that it can recognize patterns, classify/identify, predict, and optimize the use of electrical energy. Load shedding is carried out based on priority loads and non-priority loads. From testing the load shedding automation system which was carried out 5x with 6x load combination trials, it resulted that in the 1st to 4th experiments, Relay_Hidup3 output was obtained. To carry out the load shedding process, the average delay time is 2,302 seconds

关键词： Fuzzy logic Automation Mechatronics Load shedding Smart meters Delays pattern recognition

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Characteristics of feedback postural control induced by unexpected surface perturbations in elite skiers

Characteristics of feedback postural control induced by unex...

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2012 4th international conference on bioinformatics and Biomedical Technology（ICBBT 2012）

作者： Tadayoshi Asaka Hiroki Mani Tatuya Izumi Tomoya Konishi Mina Samukawa Keizo Yamamoto Kazuhiko Watanabe Faculty of Health Sciences Hokkaido University Graduate School of Health Sciences Hokkaido University

the purpose of this study was to examine superior feedback postural control following postural disturbance. Nine elite ski jumpers and nine control subjects were required to maintain balance without stepping following unexpected horizontal surface perturbation in a forward or backward direction. Unilateral kinematic data in the sagittal plane using a motion-capture system and surface electromyogram signals from ten trunk and leg muscles were recorded. A reduction in and reproducible peak magnitude of the center of mass (COM) velocity was shown in the athlete group compared to the control group. Cross-correlation analyses showed longer time lags at the moment of peak correlation coefficient between trunk flexor and extensor muscle activities, and the shorter time lags and higher correlations between ankle flexor and extensor muscle activities in the athlete group compared to the control group. We conclude that elite ski jumpers show superior balance performance following surface perturbations, more reciprocal patterns in agonist-antagonist pairs of proximal postural muscles and more co-contraction patterns in those of distal postural muscles during automatic postural responses compared to control individuals. this atypical strategy may be useful for effective balance recovery in an environment with a dynamically changing surface.

关键词： postural control, ski jumpers, muscle activation pattern

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the relative vertex clustering value - a new criterion for the fast discovery of functional modules in protein interaction networks

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BMC bioinformatics 2015年第Sup4期16卷 1-14页

作者： Ibrahim, Zina M. Ngom, Alioune Kings Coll London Inst Psychiat London SE5 8AF England NIHR Biomed Res Ctr Mental Hlth South London England Biomed Res Unit Dementia South London England Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada Maudsley NHS Fdn Trust London SE5 8AZ England

Background: Cellular processes are known to be modular and are realized by groups of proteins implicated in common biological functions. Such groups of proteins are called functional modules, and many community detection methods have been devised for their discovery from protein interaction networks (PINs) data. In current agglomerative clustering approaches, vertices with just a very few neighbors are often classified as separate clusters, which does not make sense biologically. Also, a major limitation of agglomerative techniques is that their computational efficiency do not scale well to large PINs. Finally, PIN data obtained from large scale experiments generally contain many false positives, and this makes it hard for agglomerative clustering methods to find the correct clusters, since they are known to be sensitive to noisy data. Results: We propose a local similarity premetric, the relative vertex clustering value, as a new criterion allowing to decide when a node can be added to a given node's cluster and which addresses the above three issues. Based on this criterion, we introduce a novel and very fast agglomerative clustering technique, FAC-PIN, for discovering functional modules and protein complexes from a PIN data. Conclusions: Our proposed FAC-PIN algorithm is applied to nine PIN data from eight different species including the yeast PIN, and the identified functional modules are validated using Gene Ontology (GO) annotations from DAVID bioinformatics Resources. Identified protein complexes are also validated using experimentally verified complexes. Computational results show that FAC-PIN can discover functional modules or protein complexes from PINs more accurately and more efficiently than HC-PIN and CNM, the current state-of-the-art approaches for clustering PINs in an agglomerative manner.

关键词： Protein Complexes Weighted Networks Functional Modules Network Clustering Criterion Protein Interaction Networks

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Simple Synthetic Memories of Robotic Touch 12th

Simple Synthetic Memories of Robotic Touch

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Proceedings of the 12th international conference on Biomimetic and Biohybrid Systems, Living Machines 2023

作者： Salazar, Pablo J. Prescott, Tony J. Department of Computer Science and Sheffield Robotics University of Sheffield SheffieldS1 4DP United Kingdom

ISBN: (纸本)9783031388569

It has been previously demonstrated in robots that the mimicking of functional characteristics of biologic memory can be beneficial for providing accurate learning and recognition in circumstances of social human-robot-interaction. the effective encoding of social and physical salient features has been demonstrated through the use of Bayesian Latent Variable Models as abstractions of memories (Simple Synthetic Memories). In this work, we explore the capabilities of formation and recall of tactile memories associated to the encoding of geometric and spatial qualities. Compression and pattern separation are evaluated against the use of raw data in a nearest neighbour regression model, obtaining a substantial improvement in accuracy for prediction of geometric properties of the stimulus. Additionally, pattern completion is assessed with the generation of ‘imagined touch’ streams of data showing similarities to real world tactile observations. the use of this model for tactile memories offers the potential for robustly perform sensorimotor tasks in which the sense of touch is involved. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Encoding (symbols)

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Computer Fast Two-dimensional DCT Algorithm

Computer Fast Two-dimensional DCT Algorithm

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2014 4th international conference on Education and Education Management(EEM 2014)

作者： Lizheng Cheng Hongping Li Hunan International Economics University

the author in this paper puts forward a 2D DCT algorithm and its optimized VLSI designthat is, split the two-dimensional DCT into 2 one-dimensional DCT implementations, and we only need a one-dimensional DCT processing unitAnd then through the simplification of the transformed coefficient matrix, with the adoption of the pipeline technique, use 4 multipliers to make the circuit reach the high speedthe structures of this circuit have the advantages of modular, simple wiring, and small chip areas and so on, and the experimental results show the effectiveness of the design of VLSIthe discrete cosine transformation was first proposed as the frequency domain transformation, in the pattern recognition and the Wiener filtering of the image processing, and the DCT is widely used in the image compression, the speech compression and the digital watermarking and other technologiesthe JPEG(Joint Photographic Experts Group), the MPEG(Moving Picture Expert Group), and the H.263 and other standards all adopted the DCT technology as the main program of the compression codingIn the JPEG algorithm, if we directly calculate the two-dimensional DCT transformation of the 8 * 8 image blocks and 36 point MDCT in the MPEG.1 Layer3 standards, the computation quantity is huge, and the traditional MCU and DSP are difficult to meet the requirements of the real-time processingthe FPGA parallel processing ability is especially suitable for the application in the field of the digital signal processingIn the past few years, the previous people have conducted a lot of work of the VLSI implementation of the DCT fast algorithmthe fast cosine transform structure based on FGA that Loeffler put forward and the nuclear structure based on the CORDIC(Coordinate Rotation Digital Computer algorithm) BHeyne proposed have the excellent performances in the hardware resource utilization and the interconnection complexitythis paper presents a parallel pipeline structure on this basis, which makes full use of the

关键词： 2D DCT algorithm VLSI design Pipeline Multiplier

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Research on Classification Method of Wavelet Entropy and Fuzzy Neural Networks for Motor Imagery EEG

Research on Classification Method of Wavelet Entropy and Fuz...

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4th international conference on Modelling,Identification and Control(ICMIC 2012)

作者： Xin Li is a full professor at Institute of Electrical Engineering, Yanshan University, China. She obtained her Ph.D. Degree in Yanshan University in 2008, in China. Her main research interest is in the area of Information Fusion and pattern recognition. Wei CUI, is a Master Degree Candidate at Institute of Electrical Engineering, Yanshan University, China. Changwu LI works in the Dalian Polytechnic University, China. His main research interest is in the area of Digital signal processing

Since traditional electrical brain signal analysis is mostly qualitative, the development of new quantitative methods is crucial for restricting the subjectivity in the study of brain signals. these methods are particularly fruitful when they are strongly correlated with intuitive physical concepts that allow a better understanding of brain dynamics [1]. Here, new method based on wavelet entropy (WE) and Fuzzy Neural Networks (FNN) is applied. the WE carries information about the degree of order/disorder associated with a multi-frequency signal. In addition, the time evolution of the WE is calculated to give information about the dynamics in the EEG records. Within this framework, the major objective of the present work was to characterize in a quantitative way functional dynamics of order/disorder microstates in short duration EEG signals. this paper has tried to use fuzzy neural networks (FNN) as a classification method, which combines fuzzy membership and neural network frame, to drive the model automatically update in training and leaning processes, thus it can be applied in the simulations of complex conditions. In this way, EEG signals under motor imagery conditions were analyzed.

关键词： BCI, wavelet entropy (WE), Fuzzy Neural Networks (FNN), motor imagery EEG

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