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4th International Conference on Robotics and Automation in Industry, ICRAI 2021

作者： Shahzad, Ali Raza Jalal, Ahmad Air University Dept. of Creative Technology Islamabad Pakistan Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9781665423434

Human Detection and tracking have become a focal area of research as it plays a major role in computer vision applications. Methods and equipment for the detection and tracking of humans are constantly being changed as advanced methodologies are being discovered. Even though this is a huge topic in the computer vision field, pedestrian detection in infrared images is relatively new. In this Research paper, we designed a new methodology for pedestrian detection, tracking, and counting in infrared images. The main objective of our research is to give machines a technique for the detection of pedestrians in real-time. Our proposed method of human detection detects and tracks pedestrians effectively and efficiently using the OCTVBS benchmark Pedestrian Infrared/visible Stereo Video Dataset. First, we used Template matching for Pedestrian detection. After detections tracking is done using Kalman filter. Lastly, headcount is acquired from head detections gained from applying Haar-like features. We evaluated our method on the OCTVBS dataset and gained detection accuracy of 91.04%, tracking accuracy of 92.47%, and count accuracy of 82.11%. The presented method should be useful for the monitoring of pedestrians in IR video surveillance. © 2021 IEEE.

关键词： Template matching

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A Real-Time Performance Model for NFT Chains

A Real-Time Performance Model for NFT Chains

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Blockchain Computing and Applications (BCCA), International Conference on

作者： K. Kentner J. Seol N. Park Computer Science Dept. Oklahoma State University Stillwater OK USA Information Technology Dept. Middle Georgia State University Macon GA USA Dept. of Computer Science Oklahoma State University Stillwater OK USA

This paper sets forth a bivariate, real-time NFT (nonfungible token) model based on a Markovian queueing process. This model is proposed with the goal of simulating and observing the performance of an NFT system as it behaves under real-time constraints, as well as to see how various on-/off-chain transaction rates, service rates, blocksizes, and deadline-miss rates affect the overall performance of the system. The system is assumed to operate at a steady state, thus allowing for each probabilistic state (which at a given time consists of ion-chain transactions and j off-chain transactions that have arrived) to be calculated using balance equations (all incoming transactions to a state equal all outgoing from the state when the relevant probabilities are considered). The Markovian assumption necessitates that the full state space must sum to 1.0, and because all states derive from $\mathrm{p}_{0,0}$ , P0,0 may be factored out and solved using this constraint. This in turn facilitates the computation of the remaining probabilities. The on-/off-chain transaction rates $\lambda_{on}$ and $\lambda_{off}$ , service rate $\mu$ , and deadline-miss rate $\lambda_{F}$ are the primary parameters within the model, and $\lambda_{F}$ is the key parameter for modeling the real-time behavior of the system $(\lambda_{F}$ represents a failure for a transaction to meet the deadline, and it causes the current state to self-loop). An analysis of the system via metrics including average wait time, average number of transactions, and throughput is presented, and the implications of the simulation on the overall performance of the system are discussed.

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Cost-Minimized Partial Computation Offloading in Cloud-Assisted Mobile Edge Computing Systems

Cost-Minimized Partial Computation Offloading in Cloud-Assis...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Nowadays, smart mobile devices (SMDs) support various computation-intensive and delay-sensitive applications, e.g., online games, and figure compression. However, SMDs have limited computing resources and battery energy and cannot execute all tasks of the above applications in a real-time manner. Cloud computing provides enormous computing resources and energy that can easily execute tasks offloaded from SMDs. However, could data centers (CDCs) are often located in remote sites, which leads to long transmission time. Small base stations (SBSs) offer high-bandwidth and low-latency services for SMDs, which solves the problem of cloud computing. However, it becomes a challenge to achieve the lowest cost in such a heterogeneous architecture including multiple SMDs, SBSs, and the CDC while meeting delay requirements of tasks. This work proposes a cost-minimized computation offloading strategy to minimize the total cost of the system. A constrained optimization problem is first formulated based on the hybrid architecture. Afterward, a two-stage optimization algorithm called a Lévy flights and Simulated Annealing-based Grey wolf optimizer (LSAG) is developed to optimize the total cost of the system. In the first stage, the optimal edge selection policy is determined given multiple available SBSs. In the second stage, task offloading and resource allocation among SMDs, SBSs, and the cloud are determined. Experiments with real-life tasks prove that LSAG significantly achieves lower cost with faster convergence speed than state-of-the-art peers. © 2023 IEEE.

关键词： cloud computing computation offloading intelligent optimization algorithms Lévy flights Mobile edge computing

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Web Traffic Anomaly Detection Using a Hybrid Spatio-temporal Neural Network

Web Traffic Anomaly Detection Using a Hybrid Spatio-temporal...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Xu, Lifeng Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Engineering Dept. of Computer Science in Lyle Southern Methodist University DallasTX United States

ISBN: (纸本)9798350337020

Nowadays, rapid development of Internet has brought a sharp increase in traffic data. Abnormal traffic haS serious impact on network security. Traffic anomaly detection can be achieved by extracting characteristics of network traffic to detect anomalous intrusions, and therefore, anomaly detection algorithms are of great significance to maintenance of network security. This work proposes a hybrid spatio-temporal neural network with attention named CTGA to effectively identify anomalous traffic. CTGA combines a Convolutional neural network (CNN), a Temporal convolutional network (TCN), a bidirectional Gated recurrent unit network (BiGRU), and a self-Attention mechanism. It automatically extracts temporal and spatial features of sequences from raw data by sliding window preprocessing followed by CNN, TCN, BiGRU, and the self-attention mechanism to detect anomalous data. CNN is used to extract spatial features of time sequences and reduce the loss of spatial information. In the sequence, TCN obtains short-term features. Long-term dependencies in the data are captured by BiGRU, and the self-attention mechanism obtains important information in the sequence. Finally, experiments with the real-life Yahoo S5 dataset prove that CTGA outperforms other approaches substantially. © 2023 IEEE.

关键词： Anomaly detection bidirectional gated recurrent unit convolutional neural network self-attention temporal convolutional network

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An Evaluation and Comparison for Phishing Attack Detection using Machine Learning Approaches

An Evaluation and Comparison for Phishing Attack Detection u...

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Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), 2020 International Conference on

作者： Ajeet Kumar Sharma Anushree Nitin Rakesh Pawan Kumar Verma Dept. of Computer Science & Engineering School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Dept. of Computer Engineering & Applications GLA University Mathura Uttar Pradesh India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

The persistent and evolving threat of phishing attacks demands effective and adaptive detection techniques. This research paper presents a comprehensive evaluation and comparison of various machine learning approaches to detect phishing attacks. We investigated five prominent algorithms: Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Naive Bayes, and Extreme Gradient Boosting (XGBoost), to determine their efficacy in identifying phishing activities. Our methodology involved a systematic analysis using a large dataset of phishing and legitimate URLs, where each model was trained, tested, and validated to ensure robustness and reliability. The performance of each algorithm was assessed based on accuracy, precision, recall, and F1 score. Among the evaluated models, XGBoost demonstrated superior performance, achieving an exceptional accuracy of 99.75%. This result underscores the potential of XGBoost in phishing attack detection, offering a promising tool for cybersecurity applications.

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Foreign Exchange Market Analysis based on Novel Grey Wolf based Extreme Learning Machine Predictor

Foreign Exchange Market Analysis based on Novel Grey Wolf ba...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Swaty Dash Pradip Kumar Sahu Debahuti Mishra Dept. of Information Technology Veer Surendra Sai University of Technology Burla Sambalpur Odisha India Dept. of Computer Science & Engineering Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar Odisha India

Foreign exchange trading basically bridges a gap between buyer and seller to transact at a set of prices of the currencies to make profit out of it by the traders and investors. In this paper, foreign exchange predictive models are explored based on basic Extreme Learning Machine (ELM) along with optimized version of ELM. Initially, this work tried to obtain the best optimized ELM network based upon the four nature-inspired optimization methodologies such as; particle swarm, ant colony, moth flame and grey wolf optimization (GWO). From this phase, it has been observed that the ELM network optimized with GWO outperforms rest of other experimented optimized ELM networks with respect to error graphs and performance metrics. In order to evaluate the performance, the ELM-GWO currency exchange predictor, the recognition performance metrics such as; EVS, MAE, MSE and R 2 Score are computed, the error graphs are obtained and the average computational time taken are compared.

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Enhanced Ant Colony Optimization with Dynamic Evaporation Rate (EACO-DE): An Intelligent Congestion Control System in IoV Environment

Enhanced Ant Colony Optimization with Dynamic Evaporation Ra...

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Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CCHESS), International Conference on

作者： Sudarsan Sathishkumar Amrishkanna R B. Nithya Dept. of Computer Science and Engg. Saranathan College of Engineering Trichy Tamil Nadu India Dept. of Computer Science and Engg. National Institute of Technology Trichy Tamil Nadu India

Internet of Vehicle (IoV) is a combined form of Internet of Things (IoT) and Vehicular Ad-hoc Network (VANET). Its fundamental goal is to increase the service quality of Intelligent Transportation System (ITS). The dynamic nature of IoV such as high dynamic topology, high mobility of vehicles, etc. are some of the factors that causes more congestion thus reducing the efficiency of routing. Many algorithms were designed to obtain the shortest path from the source to the destination. Choosing of Ant Colony Optimization (ACO) algorithm is one of the best ways to obtain the shortest path. In this paper, an Enhanced Ant Colony Optimization with Dynamic Evaporation rate (EACODE) algorithm is proposed to obtain the congestion-free optimized path which reduces the travel time, travel cost and traffic problems. Instead of fixed evaporation rate, it dynamic predicts the pheromone evaporation rate with the help of run time metrics to avoid the congested paths thus improving the efficiency of the travel. The simulation results show that the proposed EACO-DE algorithm improves the efficiency of the routing compared with primitive Ant Colony Optimization (ACO) algorithm and Enhanced Hybrid Ant Colony Optimization Routing Protocol (EHACORP) under various performance metrics.

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An Actor-Critic Method for Simulation-Based Optimization

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IFAC-PapersOnLine 2022年第11期55卷 7-12页

作者： Kuo Li Qing-Shan Jia Jiaqi Yan Dept. of Automation BNRist Tsinghua University Beijing China Dept. of Computer Science Tokyo Institute of Technology Japan

In this work, we study simulation-based optimization, where the agent aims to select the best configuration from the design space with as few as possible iterations. Inspired by the success of deep reinforcement learning (DRL), we formulate the sampling process as policy searching and give a solving method from the perspective of policy iteration. Concretely, a surrogate model for predicting the performance of each configuration and a parameterized sampling policy are applied, which correspond to the critic and actor in actor-critic (AC) method, respectively. We further derive the updating rule and propose two algorithms for configuration selection in continuous and discrete design spaces, respectively. Finally, the algorithms are validated experimentally on 1) two toy examples to intuitively explain the principle and 2) two high-dimensional tasks to reveal the effectiveness in large-scale problems. The results show that the proposed algorithms can efficiently deal with large-scale problems and effectively eliminate sub-optimal configurations.

关键词： simulation-based optimization reinforcement learning policy searching actor-critic policy iteration

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Evaluation of Digital Wallet Transaction Accuracy using Machine Learning 2

Evaluation of Digital Wallet Transaction Accuracy using Mach...

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2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Pallivalappil, Abdul Shareef Sinha, Divyanshu Kumari, P. Lalitha Katti, Anvesha Sakthi, G. Narkhede, Mayur Ravindra College of Computer Science and Information Science Srinivas University Mangalore India Uttarakhand Technical University Dept. of CSE Dehradun India Visakha Institute of Engineering & Technology Dept. of CSE AP Visakhapatnam India The NorthCap University Dept. of CSE Haryana Gurgaon India Hindusthan College of Engineering and Technology Dept. of CSE TN Coimbatore India Vishwakarma Institute of Technology Dept. of Ai Coe Pune India

ISBN: (纸本)9781665437899

Non-card retail payments, such as direct-from-the-bank programs and E-wallets, have become increasingly common at both brick-and-mortar and online establishments. In recent years, the use of mobile wallets has increased. It relates to any form of computer program that can 'learn' on its own beyond the intervention of a human. Machine learning can detect out-of-the-ordinary purchases and flag them as fraudulent by continuously monitoring data in real-time. Machine learning models are being improved by developing new methodologies based on pre-existing models. An example is a hybrid model in which the dataset is under-sampled and over-sampled;for example, credit card transactions. Digital wallets and machine learning are the main focus of this article. © 2022 IEEE.

关键词： Deep learning

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Sentiment Analysis of Reddit Posts Using the BERT Model in Peer-to-Peer Networks

Sentiment Analysis of Reddit Posts Using the BERT Model in P...

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Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Prasant Kumar Dash Tithi Singh Rabi Shankar Panda Ritika Budhiraja Chinmoyee Das Rajshree Swain Dept. of Computer Science and Engineering C.V Raman Global University Bhubaneshwar Odisha Dept. of Computer Science and Engineering Dr.Akhilesh Das Gupta Institute of Professional Studies New Delhi Dept. of Cognitive & Brain Sciences Indian Institute of Technology Gandhinagar Gandhinagar Gujarat

ISBN: (数字)9798331522612

ISBN: (纸本)9798331522629

Sentiment analysis, a key area in Natural Language Processing (NLP), involves categorizing text data based on its emotional tone-positive, negative, or neutral. With the growing reliance on online interactions, understanding sentiments expressed in text is vital for assessing user opinions, behaviours, and engagement. In peer-to-peer (P2P) networks, where content sharing and decentralized user interaction dominate, sentiment analysis can uncover critical insights into digital relationships and collaborative tendencies. This paper explores sentiment analysis within P2P platforms using the BERT (Bidirectional Encoder Representations from Transformers) algorithm, a state-of-the-art NLP model. Unlike traditional methods, BERT effectively captures contextual and nuanced sentiments, enabling more accurate classification. The methodology includes preprocessing data, extracting embeddings using BERT, and employing fine-tuned models for sentiment categorization. Dimensionality reduction and visualization techniques further reveal patterns, sentiment clusters, and alignment between emotional tones in user interactions. Results demonstrate that BERT-powered sentiment analysis identifies content trends, emotional polarities, and behavioural dynamics in decentralized environments. The research also addresses challenges such as handling diverse content and biases in sentiment interpretation. This study highlights the growing need for advanced sentiment analysis techniques to enhance content profiling, trend forecasting, and user understanding on decentralized platforms, offering valuable implications for businesses and researchers.

关键词： Dimensionality reduction Sentiment analysis Analytical models Accuracy Bidirectional control Market research Encoding Peer-to-peer computing Forecasting Business

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