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8th international conference on Computers Communications and Control, ICCCC 2020

8th International Conference on Computers Communications and...

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8th international conference on Computers Communications and Control, ICCCC 2020

ISBN: (纸本)9783030536503

the proceedings contain 26 papers. the special focus in this conference is on Computers Communications and Control. the topics include: Experimenting with beta distributions for approximating hammocks’ reliability;general frameworks for designing arithmetic components for residue number systems;the shape of the reliability polynomial of a hammock network;Queuing theory application on DTN buffer management;Redesign of a conference from in-person to online. case study: ICCCC;proposal of a multi-standard model for measuring maturity business levels with reference to information security standards and controls;IT solutions for big data processing and analysis in the finance and banking sectors;automatons immersed in ocean currents for transformation of biomass into fuel;challenges porting blockchain library to opencl;using two-level context-based predictors for assembly assistance in smart factories;visual analysis of multidimensional scaling using geogebra;edge computing in real-time electricity consumption optimization algorithm for smart grids;the study of trajectories of the development of state capacity using ordinal-invariant pattern clustering and hierarchical cluster analysis;a micro simulation approach for a sustainable reduction traffic jam;empirical versus analytical solutions to full fuzzy linear programming;on recursively defined combinatorial classes and labelled trees;critical analysis of faults in operation of energy systems using fuzzy logic;fuzzy-logic based diagnosis for high voltage equipment predictive maintenance;making a multi-criteria analysis model for choosing an erp for smes in a km world;a multi-objective model for devices procurement with application in health care.

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Basic Research on Machine Vision Underpinned by Image Frame Algebra (VFA) and Visual Semantic Algebra (VSA)

Basic Research on Machine Vision Underpinned by Image Frame ...

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international conference on Machine Vision and Information Technology (CMVIT)

作者： Yingxu Wang President International Institute of Cognitive Informatics and Cognitive Computing (I2CICC) Visiting Professor: Stanford Univ. (2008|2016) MIT (2012) UC Berkeley (2008) Oxford Univ. (1995|2018–2022) Distinguished Visiting Professor Tsinghua Univ. (2019–22) Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary 2500 University Drive NW Calgary Alberta Canada

Computer vison [1], [2], [3], [4], [5] studies properties of machine vision, its semantic understanding, and general manipulations by Intelligent Mathematics (IM) [6], [7], [8], [9], [10] [11], [12], [13], [14], [15] [16], [17]. Computer vison has been studies from various aspects such as algorithmic methods, analysis methods, pattern recognitions, and neural-network-regression (AI) technologies [2], [3]. However, there is a lack of fundamental theories for enabling autonomous image recognition and processing by machines. Basic research on contemporary IM has revealed that formal manipulations of visual objects by intelligent machines may be rigorously implemented by Image Frame Algebra (IFA) [8], [18] in the front-end and Visual Semantic Algebra (VSA) [19] in the backend. IFA formally manipulates visual images as general 2D matrixes by a set of algebraic operators such as modeling, analyses, syntheses, feature elicitation, and pattern recognition [4], [5], [18]. then, its counterpart, VSA, transforms the geographic relations of visual objects to their semantic interpretations by algebraic analyses and compositions. the coherent theory of IFA and VSA provides a formal methodology for machine-enabled image processing and comprehension. this keynote presents a theoretical framework of machine vision underpinned by IFA and VSA for the structural denotations of visual objects and functional manipulations of visual mechanisms [3], [8], [9]. It demonstrates how the persistent challenges to machine vision may be rigorously and efficiently solved by the IFA/VSA methodology. Case studies on applying IFA/VSA for rigorous visual pattern detection, recognition, analysis, and composition in real world will be demonstrated [5], [18], [20]. As two coherent paradigms of IM, among others [21], [22], [23], [24], [25] [26], [27], [28], [29], [30], IFA and VSA have been applied not only in robot visual and spatial reasoning, but also in computational intelligence and AI for rigorously

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8th international Symposium on Security in computing and Communications, SSCC 2020

8th International Symposium on Security in Computing and Com...

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8th international Symposium on Security in computing and Communications, SSCC 2020

ISBN: (纸本)9789811604218

the proceedings contain 21 papers. the special focus in this conference is on Security in computing and Communications. the topics include: Benchmarking Behavior-Based Intrusion Detection Systems with Bio-inspired Algorithms;thermal Management in Large Data Centres: Security threats and Mitigation;Anomaly Detection in CAN-BUS Using pattern Matching Algorithm;conjunctive Keyword Forward Secure Ranked Dynamic Searchable Encryption Over Outsourced Encrypted Data;performance Study of Multi-target Tracking Using Kalman Filter and Hungarian Algorithm;saaS - Microservices-Based Scalable Smart Contract Architecture;GIDS: Anomaly Detection Using Generative Adversarial Networks;Audio Steganography Using Multi LSB and IGS Techniques;A Forensic Analysis on the Availability of MQTT Network Traffic;a Communication-Induced Checkpointing Algorithm for Consistent-Transaction in Distributed Database Systems;the Concerns of Personal Data Privacy, on Calling and Messaging, Networking Applications;analysis of Orthogonal Time Frequency Space Transceiver in Baseband Environment;evading Static and Dynamic Android Malware Detection Mechanisms;multilevel Secure Container Deployment Framework in Edge computing;deep Hierarchical App Recommendation with Dynamic Behaviors;New Security Architecture of Access Control in 5G MEC;a Fast Authentication Scheme for Cross-Network-Slicing Based on Multiple Operators in 5G Environments;A Survey of Security Attacks on Silicon Based Weak PUF Architectures;on the Feasibility of DoS Attack on Smart Door Lock IoT Network.

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19th international conference on Hybrid Intelligent Systems, HIS 2019, and the 14th international conference on Information Assurance and Security, IAS 2019

19th International Conference on Hybrid Intelligent Systems,...

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19th international conference on Hybrid Intelligent Systems, HIS 2019, and the 14th international conference on Information Assurance and Security, IAS 2019

ISBN: (纸本)9783030493356

the proceedings contain 43 papers. the special focus in this conference is on Hybrid Intelligent Systems. the topics include: Heterogeneous engineering in intelligent logistics;welcome message;dimension reduction with extraction methods (principal component analysis - Self organizing map - Isometric mapping) in indonesian language text documents clustering;extracting unknown repeated pattern in tiled images;convolutional deep learning network for handwritten arabic script recognition;diversity in recommendation system: A cluster based approach;contribution on arabic handwriting recognition using deep neural network;analyzing and enhancing processing speed of k-medoid algorithm using efficient large scale processing frameworks;multiple criteria fake reviews detection based on spammers’ indicators within the belief function theory;data clustering using environmental adaptation method;soft computing, data mining, and machine learning approaches in detection of heart disease: A review;a novel cad system for breast dce-mri based on textural analysis using several machine learning methods;an adversarial learning mechanism for dealing with the class-imbalance problem in land-cover classification;reducing data volume in instance based learning;an integrated fuzzy anp-topsis approach to rank and assess e-commerce web sites;implementation of block chain technology in public distribution system;chaotic salp swarm optimization using svm for class imbalance problems;three-layer security for password protection using rdh, aes and ecc;clothing classification using deep cnn architecture based on transfer learning;identification of botnet attacks using hybrid machine learning models;congestion control in vehicular ad-hoc networks (vanet’s): A review;advances in cyber security paradigm: A review;recursive tangent algorithm for path planning in autonomous systems.

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Elements of Optimizing User Engagement in E-Commerce Website 8th

Elements of Optimizing User Engagement in E-Commerce Website

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8th international conference on Computational Science and Technology, ICCST 2021

作者： Rosclaritha Maidom, Lizawati Baharum, Aslina Ismail, Rozita Fatah, Nur Shahida Ab Omar, Muhammad Noor, Noorsidi Aizuddin Mat Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Kota Kinabalu88400 Malaysia College of Computing and Informatics Universiti Tenaga Nasional Putrajaya Campus Malaysia Faculty of Business Economics and Accountancy Universiti Malaysia Sabah Sabah Kota Kinabalu88400 Malaysia Department of Technology and Supply Chain Management Studies Faculty of Business and Management Universiti Teknologi MARA Cawangan Selangor Selangor42300 Malaysia Faculty of Built Environment and Surveying UTM CRES Universiti Teknologi Malaysia Johor Bahru Johor Malaysia

ISBN: (纸本)9789811685149

E-Commerce companies are faced with a highly competitive environment today. Strengthening and optimizing the user engagement elements in an E-Commerce is a promising approach to increasing value co-creation. However, the lagged of attention to the recognition of user oriented emotional needs and expansion of user-driven demands in E-Commerce will eventually deteriorate business using the platform. theoretical foundation of user engagement research is also still relatively underdeveloped. the purpose of this research is to explore and validate the user engagement attributes as it applies to peoples’ experiences with technology. By building the attributes that asserts the theory of engagement and recognised fundamental attributes, the reliability factors were then assessed for validation. Preliminary study and reliability analysis were used for the identification of user engagements that would benefit E-Commerce. the result of this research reveals six attributes of user engagements and the reliability of the constructs was computed and have an alpha greater than 0.70 which presents excellent internal consistency reliability. Results indicate user engagement is a process comprised of six focused attention, perceived usability, aesthetics, endurability, novelty, and involvement. the process is characterized by attributes of engagement that pertain to the user, the system, and user-system interaction. this research provides the foundation for future work to further explore the conceptual model in various application areas, and to develop methods to measure engaging user experiences. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reliability analysis

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Semantic Matching Algorithm for Legal Issues Based on BERT and Graph Convolution with Multi-granularity Features 23

Semantic Matching Algorithm for Legal Issues Based on BERT a...

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proceedings of the 2023 12th international conference on computing and pattern recognition

作者： Baohua Qiang Shijie Pang Keliang Teng Zhiguang Liu Weigang Liu Guilin University of Electronic Technology China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC China

ISBN: (纸本)9798400707988

In recent years, pre-training models represented by BERT have shown amazing text semantic representation capabilities supported by large-scale corpus, and now have become one of the mainstream solutions for semantic matching tasks in legal Q&A systems. However, pre-training models generally do not set specific pre-training tasks in terms of syntactic information, but implicitly and incidentally learn in large-scale unsupervised, which does not make good use of syntactic information as an important factor for representing semantics. To address this problem, this paper proposes a multi-granularity feature semantic matching model based on BERT and graph convolution. the model first obtains syntactic features of sentences in the form of dependent syntactic trees using existing tools, and then fuses syntactic features with word features and sentence features of BERT using an attention mechanism and graph convolutional neural network to enhance the characterization of text semantics and make the model better for semantic matching tasks. Experimental results show that the algorithm achieves significant improvements in the semantic matching task and outperforms the five comparison methods in the DIAC2019 dataset. Compared to the baseline model Sentenct-BERT, the accuracy, precision, recall and F1 values are improved by 0.33%, 1.18%, 1.03% and 1.05% improvement.

关键词： BERT Graph Convolutional Neural Network Legal Q&A Semantic matching

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5th international conference on Intelligent computing and Applications, ICICA 2019

5th International Conference on Intelligent Computing and Ap...

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5th international conference on Intelligent computing and Applications, ICICA 2019

ISBN: (纸本)9789811555657

the proceedings contain 71 papers. the special focus in this conference is on Intelligent computing and Applications. the topics include: Performance Estimation and Analysis Over the Supervised Learning Approaches for Motor Imagery EEG Signals Classification;fully Automated Digital Mammogram Segmentation;empirical Study of Computational Intelligence Approaches for the Early Detection of Autism Spectrum Disorder;Intelligent Monitoring of Bearings Using Node MCU Module;image Denoising Using Various Image Enhancement Techniques;energy Consumption Analysis and Proposed Power-Aware Scheduling Algorithm in Cloud computing;relationship Between Community Structure and Clustering Coefficient;digital Image Forensics-Image Verification Techniques;energy Efficient Data Centre Selection Using Service Broker Policy;using Automated Predictive Analytics in an Online Shopping Ecosystem;design and Development of Home Automation System;performance Analysis Study of Stochastic computing Based Neuron;Scheduling of Parallel Tasks in Cloud Environment Using DAG MODEL;GREENIE—Smart Home with Smart Power Transmission;ANAVI: Advanced Navigation Assistance for Visually Impaired;thIRD EYE—Shopfloor Data Processing and Visualization Using Image recognition;A Hybrid Search Group Algorithm and pattern Search Optimized PIDA Controller for Automatic Generation Control of Interconnected Power System;Fractional Order PID Controlled PV Fed Quadratic Boost Converter TZ Source Inverter Fed Permanent Magnet Brushless Motor Drive;co-ordinate Measurement of Roll-Cage Using Digital Image Processing;peformance Analysis of Joysticks Used in Infotainment Control System in Automobiles;Probabilistic Principal Component Analysis (PPCA) Based Dimensionality Reduction and Deep Learning for Cancer Classification;blockchain Technology for Data Sharing in Decentralized Storage System;Understanding Concepts of Blockchain Technology for Building the DApps;chennai Water Crisis—Data Analysis.

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IoT-Enabled Dual-Sensing Smart Waste Management System: Enhancing Urban Cleanliness and Sustainability in Smart Cities

IoT-Enabled Dual-Sensing Smart Waste Management System: Enha...

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Artificial Intelligence (SLAAI-ICAI), SLAAI international conference on

作者： R.M.P.M.D. Rathnayake T.M.M. Chanaka P.T.R. Dabare K.P. Hewagamage Department of Mechanical Engineering The Open University of Sri Lanka Nawala Sri Lanka Department of Electrical and Computer Engineering The Open University of Sri Lanka Nawala Sri Lanka School of Computing Univercity of Colombo Colombo 3 Sri Lanka

ISBN: (数字)9798331509170

ISBN: (纸本)9798331509187

the management of trash in urban areas encounters substantial obstacles because of the increasing population density and ineffective collection methods, resulting in environmental and health risks in urban cities. Current Internet of things solutions generally concentrate on monitoring bin fill levels, but fail to include important factors such as garbage decomposing and route optimization. this study presents a novel data-driven artificial intelligence method for garbage management by combining dual-sensing smart bins with real-time analytics. the system we use utilizes ultrasonic sensors to detect the fill-level and methane sensors to monitor spoiling in bins located in Dehiwala, Sri Lanka. the data is transmitted by ESP32 WiFi modules to a cloud-based platform. An individualized Node-RED integration enables the processing of data, while machine learning models enhance the efficiency of collection routes and forecast trends of trash generation. During the pilot implementation involving three smart bins over a four-month duration, the system exhibited several critical capabilities: precise real-time fill-level detection with 95% accuracy, dependable spoilage monitoring with 92% accuracy in methane detection, and effective pattern recognition for weekly and monthly waste generation trends. the route optimization technique demonstrated a 15% enhancement in path efficiency relative to fixed routes during simulation testing. Analysis of temporal patterns identified constant peak fill times and spoiling trends, facilitating predictive scheduling possibilities. this study demonstrates the capacity of data-driven intelligence formulate a paradigm for scalable intelligent waste management systems, showcasing the potential for biogas system integration and adaptive capacity planning.

关键词： Waste management Methane Accuracy Market research Sensor systems Real-time systems Optimization Monitoring Intelligent sensors Capacity planning

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Modified Contrast Enhancement Algorithm Using Numerical Integration Simpson's Method 2nd

Modified Contrast Enhancement Algorithm Using Numerical Inte...

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2nd international conference on Computational Intelligence in pattern recognition (CIPR)

作者： Halder, Amiya Sarkar, Apurba Shah, Nikita STCET Dept CSE 4 DH Rd Kolkata 23 India IIEST Shibpur Dept CST Howrah 711103 India

ISBN: (纸本)9789811524493;9789811524486

this paper presents an efficient method to improve the contrast of low contrast images and at the same time provides satisfactory performances in terms of image contrast and sharpness. It is based on numerical cubic interpolation formula known as Simpson's 3/8th rule. the neighboring pixel information is used in interpolation method, and it gives the high contrast image and also preserves fine details of the images. Different quality-based parameters are used to measure the improvement of the contrast of the images. the obtained results are compared with various standard existing methods for further validation.

关键词： Contrast enhancement Numerical integration Similarity index measure Simpson's 3/8th rule

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Anomaly Handwritten Text Detection for Automatic Descriptive Answer Evaluation 22

Anomaly Handwritten Text Detection for Automatic Descriptive...

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proceedings of the 2022 11th international conference on computing and pattern recognition

作者： Nilanjana Chatterjee Palaiahnaakote Shivakumara Umapada Pal Tong Lu Yue Lu Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Faculty of Computer Science and Information Technology University of Malaya Malaysia National Key Lab for Novel Software Technology Nanjing University China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University China

ISBN: (纸本)9781450397056

Although there are advanced technologies for character recognition, automatic descriptive answer evaluation is an open challenge for the document image analysis community due to large diversified handwritten text and answers to the question. this paper presents a novel method for detecting anomaly handwritten text in the responses written by the students to the questions. the method is proposed based on the fact that when the students are confident in answering questions, the students usually write answers legibly and neatly while they are not confident, they write sloppy writing which may not be easy for the reader to understand. To detect such anomaly handwritten text, we explore a new combination of Fourier transform and deep learning model for detecting edges. this result preserves the structure of handwritten text. For extracting features for classification of anomaly text and normal text, the proposed method studies the behavior of writing style, especially the variation at ascenders and descenders. therefore, the proposed work draws principal axis which is invariant to rotation, scaling and some extent to distortion for the edge images. With respect to principal axis, the proposed method draws medial axis using uppermost and lowermost points. the distance between the medial axis and principal axis points are considered as feature vector. Further, the feature vector is passed to Artificial Neural Network for classification of anomaly text. the proposed method is evaluated by testing on our own dataset, standard dataset of gender identification (IAM) and handwritten forgery detection dataset (ACPR 2019). the results on different datasets show that the proposed work outperforms the existing methods.

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