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Crow Search Freeman Chain Code (CS-FCC) Feature Extraction Algorithm for Handwritten Character recognition 8

Crow Search Freeman Chain Code (CS-FCC) Feature Extraction A...

引用

8th IEEE international conference on software Engineering and Computer Systems, ICSECS 2023

作者： Mohamad, Muhammad Arif Musa, Zalili Ismail, Amelia Ritahani Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pekan Pahang26600 Malaysia Kulliyyah of Information and Communication Technology International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798350310931

With so many algorithms developed to improve classification accuracy, interest in feature extraction in Handwritten Character recognition (HCR) has increased. In this research, a Crow Search Algorithm (CSA)-based metaheuristic strategy for feature extraction in HCR was developed. The data representation method employed was Freeman Chain Code (FCC). The fundamental issue with using FCC to represent a character is that the outcomes of the extractions depend on the starting points that changed the chain code's route length. The shortest route length and least amount of computational time for HCR were found using the metaheuristic technique via CSA, which was suggested as a solution to this issue. The suggested CS-FCC extraction algorithm's computation durations and route lengths serve as performance indicators. The algorithm experiments are carried out using the chain code representation created from previous research of the Centre of Excellence for Document Analysis and recognition (CEDAR) dataset, which consists of 126 upper-case letter characters. According to the results, the proposed CS-FCC has a route length of 1880.28 and only takes 1.10 seconds to solve the entire set of character images. © 2023 IEEE.

关键词： Feature extraction

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Unsupervised Discovery of Recurring Spoken Terms Using Diagonal patterns 10th

Unsupervised Discovery of Recurring Spoken Terms Using Diago...

引用

10th Biennial international conference on pattern recognition and Machine Intelligence (PReMI)

作者： Sudhakar, P. Rao, K. Sreenivasa Mitra, Pabitra Indian Inst Technol Adv Technol Dev Ctr Kharagpur 721302 India Indian Inst Technol Dept Comp Sci & Engn Kharagpur 721302 India

ISBN: (纸本)9783031451690;9783031451706

Spoken term discovery is a challenging task when a lot of spoken content is generated without annotation. The spoken term discovery task accomplished by pattern matching techniques resolves the challenge by directly capturing the resemblance of the spoken terms at the acoustic feature level. Despite feasibility, the pattern-matching approach generates more false alarms during the discovery task due to fluctuations that arise in natural speech;hence degradation in the performance was observed. In the proposed approach, the challenge that arises due to the variability is addressed in two stages. In the first stage, the RASTA-PLP spectrogram was used as an acoustic feature representation that reduces the variabilities among similar spoken contents. In the second stage, the novel Diagonal pattern Search method unconstrainedly computes the pattern resemblance between the identical spoken terms at the segmental level. The proposed approach was evaluated using the IITKGP-SDUC speech corpus and inferred that a 10.11% improvement in the accuracy was achieved compared to other state-of-the-art systems in the spoken term discovery task.

关键词： spoken term discovery Diagonal pattern Search pattern matching diagonal similarity

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Design Of Symmetric Meander Line Antenna (MLA) Operating At UHF Frequency Of 432.5 Mhz For Iot Smart Home Application

Design Of Symmetric Meander Line Antenna (MLA) Operating At ...

引用

2023 international conference on Evolutionary Algorithms and soft computing Techniques, EASCT 2023

作者： Jeba Kumar, R.J.S. Ramesh, G.P. St. Peter's Institute of Higher Education and Research Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350313413

IoT is a new way of using the inter-connected network that allows us to share information all over the world seamlessly. The process of data sharing necessitates the utilization of antennas for the broadcast and reception of data. Internet of Things (IoT) antennas are used in communications media that are interconnected. IoT antenna is one of the essential elements of IoT tele-communications. This research paper presents the design and analysis of meander antenna, operating under the Ultra High Frequency (UHF) bands at 432.5 MHz for IOT Smart Home Application. The objective of the proposed design is to achieve improved performance in terms of both bandwidth and radiation characteristics. The proposed design is compared with the simulation results to ensure its accuracy. The proposed Symmetrical Meander Line antenna for IoT Smart Home Application yields the simulation results at UHF Operating Frequency of 432.5 MHz with the Reflection Coefficient of -16 dB, VSWR value of 1.3 & Axial ratio of 0.7. © 2023 IEEE.

关键词： Antenna Internet of Things Meander Antenna Radiation pattern Ultra High Frequency.

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An Adaptive PCA-Like Asynchronously Deep Reservoir computing for Modeling Data-Driven soft Sensors 5th

An Adaptive PCA-Like Asynchronously Deep Reservoir Computing...

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5th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Bo, Yingchun Zhang, Xin China Univ Petr Coll Control Sci & Engn Qingdao 266580 Peoples R China

ISBN: (纸本)9783031189067;9783031189074

Reservoir computing (RC) is a promising tool to build data-driven models, which has exhibited excellent performance in dynamical modeling area. Asynchronously deep reservoir computing (ADRC) is an improved version of RC. It generates more diverse dynamics in the reservoir than traditional RCs because of multi-layered structure and asynchronous information process. Reservoir size is a key factor to affect the performance of ADRC. However, the reservoir size of ADRC is very difficult to be determined. To solve this problem, an adaptive PCA (principal component analysis)-like method is proposed. This method promotes the useful reservoir neuron signals while suppresses the useless reservoir neuron signals. It is very similar to the function of PCA in data process. The training way of the adaptive PCA-like ADRC (APCA-ADRC) is derived based on ridge regression technique. The validity of the APCA-ADRC is tested by modeling the SO2 concentration in sulfur recovery unit. Experimental results show it is prominent in building soft sensors with high performance.

关键词： Recurrent neural networks Principal component analysis Ridge regression

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Haar Cascade Face Detection and Local Binary pattern Histogram Face recognition based Drone 8

Haar Cascade Face Detection and Local Binary Pattern Histogr...

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8th international conference on Advanced computing and Communication Systems, ICACCS 2022

作者： Shanthi, K.G. Sesha Vidhya, S. Vishakha, K. Subiksha, S. Srija, K.K. Srinee Mamtha, R. R.M.K College of Engineering and Technology Dept of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665408165

Face recognition is a popular technique that uses image processing to identify people's faces. Face recognition is becoming momentous due to the growing populace, which necessitates high security and monitoring systems, as well as the need for self confirmation in the current era, fight in rustic regions, disaster relief, and so on. This study proposes a way to assist the task force to track illicit, missing people and for surveillance employing a face recognition based drone. Haar Cascade is a face detection algorithm that is used to make out faces in images and real-time videos. Local Binary pattern Histogram (LBPH) is used to recognize a person's face. The drone yielded an accuracy of 98 percent in the identification of individuals there by finds significant applications. © 2022 IEEE.

关键词： Face recognition

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Design of Binary Neural Network soft System for pattern Detection using HDL Tool 7

Design of Binary Neural Network Soft System for Pattern Dete...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Chinchanikar, Akshay Chandankhede, Pankaj H. Titarmare, Abhijit Department of Electronics and Telecommunication Engineering G H Raisoni College of Engineering Nagpur440016 India

ISBN: (纸本)9781665464086

Convolution neural network is real time application used to perform complex image processing task. It reduces the total cost of hardware and increases its efficiency. System follows parallel scanning technique with gate level operations. It is inspired by image processing by brain's technique. Eye gathers information send it to brain & brain compute the process with sample images. Then it comes to conclusion. For hardware application GPUs and FPGAs are consider to perform or to process the imagery information because FPGA has the capability of parallel process or concurrent processing hardware facility. Objective of this paper is to design, synthesis & understand effect of different FPGA performances in BNN processer. © 2023 IEEE.

关键词： Deep neural networks

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Student Attendance recognition using the Haar Cascade Algorithm

Student Attendance Recognition using the Haar Cascade Algori...

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2023 international conference on Sustainable computing and Smart Systems, ICSCSS 2023

作者： Keerthi, Lagisetty Divya, Poleboina Sairam, U. Chaitanya Bharathi Institute of Technology Department of Information Technology Telangana India

ISBN: (纸本)9798350333602

Attendance systems are very important in schools, colleges, and many organizations. It tells about whether a student is regular to the school/college or not, an employee is regular to the work or not. The traditional method of attendance is to take manually by using roll calls and notes in the attendance register given by the organization. In some organizations, students need to sign in the register for attendance. But it is a difficult process, there may be a chance for the occurrence of proxy. It creates a lot of disturbances in class and a waste of time. Proxy leads to the irregularity of the students and decreases performance in academics. It may affect their goals and deviates into other activities. Alternating methods need to be implemented that should rectify the issues that exist with the traditional method, and save both time and manual work. © 2023 IEEE.

关键词： Local binary pattern

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pattern recognition Algorithm to Detect Suspicious Activities

Pattern Recognition Algorithm to Detect Suspicious Activitie...

引用

2023 international conference on Sustainable computing and Smart Systems, ICSCSS 2023

作者： Mahavishnu, V.C. Vikhas, S.G. Shyamganesh, T. Roopakumar, S. Kavin, S.V. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350333602

Detecting suspicious activities in public places with higher people gathering and interaction has turned out to be an act with growing interest due to the increasing number of crime scenes and causalities happening in these days. Surveying and tracking of human activities are increasingly difficult owing to the random nature of human movements and actions. The reliability is greatly affected due to this randomness. Also a human operator cannot continuously monitor multiple screens efficiently in a consequent manner so an automated surveillance system deployment becomes a necessity. Currently, tracking individuals may be done remotely, and the analysis of the recorded images can be automated using object detection models, with the help of high resolution cameras and the development of machine learning techniques. This proposed system aims in identifying threats that are probable to occur in a public gathering or space which may be an explosion, accident or possession of armoury, etc. This proposed model takes advantage of the information from the image data to learn complex patterns and develop pattern recognition technique to identify the anomalies using high resolution camera and alert the monitoring authority in order to take the necessary actions. This proposed work compares various object detection techniques of machine learning algorithms and suggests the best model based on its performance metrics. © 2023 IEEE.

关键词： Object detection

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A Tutorial on Dataset Creation for Sensor-based Human Activity recognition

A Tutorial on Dataset Creation for Sensor-based Human Activi...

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2023 IEEE international conference on Pervasive computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Reining, Christopher Nair, Nilah Ravi Niemann, Friedrich Rueda, Fernando Moya Fink, Gernot A. Chair of Material Handling and Warehousing Germany Pattern Recognition in Embedded Systems Group Germany

ISBN: (纸本)9781665453813

Well-documented, large and diverse datasets are important to facilitate supervised and transferable deep learning methods for human activity recognition. The past years have seen the machine learning community strive to achieve best practices in dataset creation for various applications. This contribution is the first to provide a comprehensive tutorial on data creation for sensor-based human activity recognition with firsthand instructions. We introduce the dataset creation process consisting of three phases as a guideline for the community. Each phase explains common challenges researchers may face and suggest solutions. A case study demonstrates how the framework can be used by researchers for holistically planning and critically reflecting their approach of dataset creation. © 2023 IEEE.

关键词： Deep learning

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Quantum Accelerated pattern Matching for Genome Sequencing in Complex RNA Secondary Structures 3

Quantum Accelerated Pattern Matching for Genome Sequencing i...

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3rd IEEE international conference on Quantum computing and Engineering (QCE)

作者： De, Rohit Del Norte High Sch San Diego CA 92127 USA

ISBN: (数字)9781665491136

ISBN: (纸本)9781665491136

Sequence matching in genomics can be approached by quantum computing through Grover's algorithm offering a quadratic speed-up versus the fastest classical algorithms. Previous research using quantum computing focused on long DNA sequences, but application to complex RNA secondary structures with hairpin, bulge, ring, and stem forms has not been possible. This paper developed new formulations for RNA structures using quantum pattern recognition and quantum Hamming distance on Grover's framework by extracting linear regions from the complex forms. To best leverage the quantum speedup, methods are developed to create long sequences by concatenating different linearized parts of the complex RNA structures. The mechanism is simulated on IBM's Qiskit quantum simulator using RNA examples from GenBank and mirBase. Comparisons of the iteration complexity indeed shows the potential for quadratic speedups from quantum computing versus classical. Simulation results also show that search accuracy is very good when using the quantum pattern recognition method.

关键词： Grover Algorithm Quantum pattern Matching Hamming Distance DNA RNA Genome Sequencing

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