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computer Vision Techniques for Object Detection

Computer Vision Techniques for Object Detection

2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Hemalatha, Raavi Amulya, Godi Lalitha, Ch.S.N. Sai Vishnu Institute of Technology Department of Computer Science & Engineering Bhimavaram534202 India

ISBN: (纸本)9798331543617

The objective of this work is to investigate the applications of computer vision techniques in the field of object identification, as well as the developments that have occurred in these techniques. The primary objective of this research is to investigate the most recent methods that have been developed to support the ability of machines to immediately detect and classify objects that are contained within images and video aqueducts. The utilization of deep literacy models and convolutional neural networks (CNNs) is utilizedto investigate these techniques. The inquiry focuses on infrastructures and algorithms, such as YOLO (which was originally known as You Only Look), SSD (which stands for Single Shot MultiBox Sensor), and Faster R- CNN, with an emphasis on their performance, delicacy, and computational efficiency. YOLO was once known as You Only Look. In addition to this, the research investigates the challenges that are experienced during the process of object detection. One of these challenges is dealing with occlusions, another is recognizing small objects, and yet another is coping with changing lighting conditions. To provide insight into the implicit advancements and new directions related to the enhancement of item-locating skills, the objective of this research is to provide information. Exhaustive testing and analysis are how this is accomplished. The findings of the investigation indicate that the contribution. © 2024 IEEE.

关键词： Convolution

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Fast Simulation of Spontaneous Parametric Down-Conversion via Neural Operators

Fast Simulation of Spontaneous Parametric Down-Conversion vi...

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Quantum 2.0, QUANTUM 2024 in Quantum 2.0 Conference and Exhibition

作者： Shacham, Dor Hay Maor, Nativ Halperin, Ben Rozenberg, Eyal Bronstein, Alex M. Freedman, Daniel Technion Israel Institute of Technology Department of Computer Science Israel Verily Research

We present a learning approach to simulating Spontaneous Parametric Down-Conversion. Based on Fourier Neural Operators, the method is both fast and resolution independent. The learned operator is able to generalize we...

ISBN: (纸本)9781557525185

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Two Fold Clustering Schema (TFCS) for Acquisition of Authentic Reviews in Web Crawlers 2nd

Two Fold Clustering Schema (TFCS) for Acquisition of Authent...

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2nd International Conference on Deep sciences for computing and Communications, IconDeepCom 2023

作者： Arthy, J. Raja, K. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai603201 India

ISBN: (纸本)9783031689048

Nowadays, people will not stop their activity once they purchase the goods on a website. They will post their experience and emotions on a website as a review. The majority of customers search for product ratings on websites;they focus only on filtered products to save time, and as a result, many spammers post fake reviews online to increase the sales of their product. For this, we propose a method known as the Two-Fold Clustering Schema (TFCS). In this method, the first fold indices the web sites using a partition clustering algorithm based upon the personalized framework to produce the customized crawler, where the websites are filtered to produce only the product review web pages posted by the customers on the e-commerce website, and then in the second fold of agglomerative clustering, reviews are grouped based on their properties, relevant features are extracted using the classifier module, where the authentic review is filtered and the remaining reviews are discarded as they are fake. In the first clustering activity, the reviews are selected, and in the second clustering, the prepared dataset of reviews produces the genuine review. Existing scraping methods have dependency issues, but in this system there are no dependency issues because it retrieves the data dynamically from search engines, so there are no dependencies and it is very efficient and accurate in terms of human generated and machine generated reviews. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sales

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Exploring The Trade-off between Efficient Service Placement Time and Optimized Fog Colonies Utilizing Advanced Genetic Algorithm with Diverse Network Topologies 24

Exploring The Trade-off between Efficient Service Placement ...

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13th International Conference on Software and computer Applications, ICSCA 2024

作者： Verma, Nilesh Kumar Khetavath, Jairam Naik Department Of Computer Science And Engineering National Institute Of Technology Raipur Raipur India

ISBN: (纸本)9798400708329

Optimized Fog colony is one of the aid of fog computing, consisting of fog devices, enable efficient management of large fog domains. Well-designed fog colonies can operate independently, resulting in effectively resource utilization and improved system performance. However, there is a lack of optimization approaches for organizing fog devices into colonies and determining which fog colony should be used to stipulated and execute the services required by an IoT application is its primary challenge known as the "efficient service placement time"(ESPT). Contrary to the prevalent practice of treating ESPT as a single objective optimization problem, which often proves insufficient for accommodating the escalating complexities of engineering practice, our study takes a different approach. We present a modeling framework that considers the ESPT in fog computing as a constrained multi-objective optimization problem. Our secondary objective is to minimize the response time of services within the fog colonies. We employ the advanced elitist non-dominated sorting genetic algorithm (MS-NSGA) to optimize the fog colonies for constrained multi-objective service placement problem. We compare our experiment for three different network topologies with various configuration. The experimental result demonstrate the best trade-off between service placement time and proposed scheme for Barabasi-Albert network topology. Additionally, the results also indicate a clear trend towards reduced response time. © 2024 ACM.

关键词： Fog

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Brain-controlled emergency calling system for paralyzed individuals 2

Brain-controlled emergency calling system for paralyzed indi...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Swetha, K.R. Fardeen, Mohammed Prakruthi, B.N. Srujan, K.M. Manoj Kumar, A.N. Department of Computer Science BGS Institute of Technology Adichuncunagiri University Mandya India

ISBN: (纸本)9798350383867

Widely utilized and well-known quantitative methods for collecting biological signals at the cortical and muscle levels, respectively, are electroencephalography (EEG) and electromyography (EMG). They offer pertinent insights for expanding knowledge across several sectors, including studies concerning neuromotor rehabilitation and physical and cognitive areas. Thus far, EEG and EMG methods have been used separately to direct or evaluate the recovery process, with a preference for one method over the other based on the purpose of the study. EEG and EMG combined have lately come to be thought of as a potentially ground-breaking method to increase the efficacy of rehabilitation. The revolutionary Brain- Controlled Emergency Calling Device for Paralyzed is designed to give those who have restricted the ability to move around a way to communicate. This endeavor uses Arduino hardware in conjunction with EMG (Electromyography) and EEG (Electroencephalography) devices to develop a dependable emergency alert mechanism. The device allows paralyzed people to send an automated message to their caregivers and caretakers using a GSM module, triggering an emergency alarm based on face muscle impulses picked up by the EMG detector. © 2024 IEEE.

关键词： Electroencephalography

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Offensive Language Detection on Telugu Language 7th

Offensive Language Detection on Telugu Language

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Kalyan, Kakollu Pavan Naveen, Valiveti Vani, V. Karthik, N. Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

ISBN: (纸本)9783031699818

In the present world a lot of data is generated via twitter, Instagram, WhatsApp etc. in different languages. It is important and necessary task to detect the offensive language among those data to create healthy and good environment among people. And it is even highly challenging task to identify offensive language in low resource languages due to less availability of the classified datasets. This paper aims to detecting of offensive language on a low resource language Telugu. To identify solution for this problem different types of ML models and DL models are used. Based on accuracy of the different models we are going to choose a model. To split the data and train the data and to test the data we have been using stratified k fold cross validation which is an efficient way to split the data and to increase model’s ability to perform better. From basis of this experiments, we can have a model to detect offensive language in Telugu and it must be considered as a small step for future models to work on it. © IFIP International Federation for Information Processing 2024.

关键词： Deep learning

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A Reliable Framework for Detection of Smart Contract Vulnerabilities for Enhancing Operability in Inter-Organizational Systems

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Journal of Mobile Multimedia 2024年第2期20卷 411-433页

作者： Arunprasath, S. Suresh, A. Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Chengalpattu Tamil Nadu Chennai 603203 India

Information and communication technology based inter-organizational systems enable companies to integrate information and conduct business electronically across different parts of the organization. For organizations embracing blockchain, smart contracts provide automation and operational efficiency for inter-organizational systems. Initially utilised for financial transactions, smart contract are extended beyond banking and deployed in wide number of organizations. Smart contracts are regarded as self-executing type of contract consisting of agreement's terms embedded directly into the code which plays a vital role in operability for inter-organizational systems, however, smart contract vulnerabilities can arise due to programming errors, leading to security issues. The effects of smart contract vulnerabilities can be significant, including loss of funds, unauthorized access to sensitive information, manipulation of data, and loss of trust in the application leading to catastrophic financial losses followed by legal implications for an organization based on blockchain technology. The goal of smart contracts exploiting vulnerabilities is to discover and eliminate potential security vulnerabilities in smart contract code prior to it being deployed. Detecting vulnerabilities in a timely manner helps to prevent financial losses, unauthorized access, and data manipulation. In order to provide a robust solution to detect vulnerabilities in smart contracts, the proposed methodology presents a novel approach for rapid detection of vulnerabilities by integrating genetic algorithm with isolation forest. Furthermore, enhancing smart contract vulnerability identification with higher accuracy and false-positive rate provides a reliable gateway for organizations to adopt blockchain. © 2024 River Publishers.

关键词： blockchain technology Ethereum smart contracts genetic algorithms Inter-organizational systems isolation forest smart contract vulnerability detection vulnerability detection

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Improved Particle Swarm optimization Task Scheduling Algorithm Based on Multiple Swarm Molecular Dynamics Theory 24

Improved Particle Swarm optimization Task Scheduling Algorit...

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5th International Conference on computer Information and Big Data Applications, CIBDA 2024

作者： Liu, Zilai Yu, Liang Li, Qiqian College of Computer Science Guangdong University of Science and Technology Guangdong AIoT Application Innovation Joint Laboratory AIoT Edge Computing Engineering Technology Research Center Dongguan523000 China

ISBN: (纸本)9798400718106

In light of the objective to optimize task scheduling in Cyber-physical systems (CPS) by establishing an efficient mapping between system resources and user tasks, a novel particle swarm optimization task scheduling algorithm is proposed. This algorithm incorporates the principles of multiple swarm molecular dynamics theory, thereby enhancing its performance. By integrating molecular dynamics theory and KMTOA into the particle swarm optimization algorithm, the proposed approach aims to achieve improved task time span and load balancing capabilities. To achieve this, the algorithm initially divides the particle population into distinct sub-populations. Subsequently, different gravitational rules are applied to guide the evolution of particles in each sub-population. This division and rule implementation greatly enhance the diversity of the population in the early stages and enhance the local search ability in the later stages. Simulation and comparative experiments have been conducted to evaluate the effectiveness of the improved task scheduling algorithm. The results demonstrate that the algorithm exhibits superior task time span and load balancing capabilities, effectively enhancing the efficiency of resource mapping. © 2024 ACM.

关键词： Cyber Physical System

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Voiceprint recognition model based on Ecapa-Tdnn and ResNet 5

Voiceprint recognition model based on Ecapa-Tdnn and ResNet

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5th International Conference on Telecommunications, Optics, and computer science, TOCS 2024

作者： Jia, Chengjie Fei, Rong Li, Xiaomeng School of Computer Science and Engineering Xi'an University of Technology Shaanxi Xi'an710004 China Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Shaanxi Xi'an710048 China

ISBN: (纸本)9781510691667

In recent years, with the continuous progress of deep learning neural networks and the construction of large data sets, new breakthroughs in voiceprint verification technology for voice calls have been rapidly achieved. Deep learning-based neural networks, such as X-Vectors, ResNets, Ecapa-Tdnn, etc., are able to utilize their own network characteristics to extract rich voiceprint features, thus further improving the accuracy of voiceprint recognition, but there is still room for improvement in the performance and parameter count of the traditional models. In this paper, we propose the Ecapa2Tdnn model based on EcapaTdnn and ResNet, which has the advantage of possessing a deeper network architecture while performing better in terms of temporal modeling capability. This indicates that the model can effectively handle long time series of speech data, thus improving the performance of the voiceprint recognition system. The experimental results show that under the same experimental environment, compared with the equal error rate (EER) of 11.31% of the EcapaTdnn model, the Ecapa2Tdnn model proposed in this paper has only an EER of 10.27%, which is 1.04% lower than the former EER, and the number of parameters is still one-fourth of the original model. The model improves accuracy while reducing the total number of parameters, which is expected to be widely used in the field of voiceprint recognition. © 2025 SPIE.

关键词： Deep neural networks

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Design and implementation of cooperative multi-agent based on MAPPO 5

Design and implementation of cooperative multi-agent based o...

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5th International Conference on Telecommunications, Optics, and computer science, TOCS 2024

作者： Wang, Haolong Fei, Rong Chen, Ruizhu School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shannxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China

ISBN: (纸本)9781510691667

In the past few years, with the continuous development of deep reinforcement learning, the application of deep reinforcement has been set off within the realms of self-driving vehicle technology, game games, and natural language processing, and the proximal policy optimization algorithm has emerged as a focal point in the sphere of deep reinforcement learning research. In this paper, the Proximal Policy Optimization (PPO) algorithm and the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm are studied, and a cooperative multi-agent system is designed and implemented based on the MAPPO algorithm in the field of StarCraft game, which is compared with the popular Q-Value Mixing (QMIX) reinforcement learning algorithm in recent years, and proposes a direction for subsequent system improvement. In the proximal policy optimization algorithm, the traditional policy gradient algorithm, natural policy gradient algorithm, trust domain policy optimization algorithm, proximal policy optimization algorithm, and independent proximal policy optimization algorithm are studied. In the multi-agent proximal policy optimization algorithm, the centralized training decentralized execution architecture, athlete critic method, value standardization, experience playback, and GAE (GAE) adopted by the MAPPO algorithm are studied. Designing and implementing a cooperative multi-agent system, introducing the StarCraft environmental campaign. Regarding the specifics of the training process, the policy gradient algorithm is used to update the policy network, the time series difference algorithm is improved, and the generalized advantage estimation is used to update the value network. Compared with the QMIX learning algorithm, the model trained by the MAPPO algorithm has the advantages of faster convergence and better stability. © 2025 SPIE.

关键词： Deep reinforcement learning

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