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A Framework for on Road Vehicle Counting and Detection

A Framework for on Road Vehicle Counting and Detection

引用

2023 international conference on Recent advances in science and engineering technology, ICRASET 2023

作者： Kumar, Tinu Kaur, Bobbinpreet Sharma, Sourabh Sheenam Chandigarh University Department of Computer Science and Engineering Mohali140413 India

ISBN: (纸本)9798350306927

This term paper looks at vehicle location procedures that can be utilized for activity observing frameworks. The framework works in conjunction with CCTV camera integration to identify vehicles. The first step is always automatic object detection. Hair cascade is used to detect cars in video. We train these cascade classifiers using the Viola-Jones algorithm. Adjust to discover special objects in recordings by following each car in a chosen locale of intrigued. It is one of the speediest ways to precisely distinguish, track and check car objects with up to 78% precision. © 2023 IEEE.

关键词： Computer vision

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Intelligent Fault Detection and Prediction in Smart Grids Using Supervised Learning Model

Intelligent Fault Detection and Prediction in Smart Grids Us...

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2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Usha, S.M. Kumar, D. Mahesh Kavitha, M. Pavithra, G.S. Mallikarjunaswamy, S. Sharmila, N. Rekha, V. Umashankar, M.L. Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Department of Computer Science and Engineering Bangalore India Wipro Ltd Wipro Engineering Edge Department Doddakannelli Bengaluru India

ISBN: (纸本)9798350388602

The integration of renewable energy sources and distributed power generation in smart grids has significantly increased the complexity of fault detection and prediction. Traditional methods like Supervisory Control and Data Acquisition (SCADA) and Rule-Based Detection (RBD) face challenges in processing the vast amounts of real-time data generated by modern grids, often leading to delayed responses and low fault detection accuracy. Furthermore, conventional techniques such as Fault Tree Analysis (FTA) are rule-dependent and lack adaptability to dynamic grid conditions, resulting in inefficient fault prediction and grid stability issues. To overcome these challenges, this paper introduces an Intelligent Fault Detection and Prediction Model (IFDPM), utilizing supervised learning techniques to enhance the detection and prediction of faults in smart grids. The IFDPM leverages machine learning algorithms to process real-time grid data, improving both the accuracy and speed of fault identification. By continuously adapting to the grid's operational environment, the proposed model predicts faults before they occur, enabling timely preventive actions. Through extensive simulations and testing, the IFDPM has demonstrated a 0.25% improvement in fault detection accuracy and a 0.20% reduction in response time compared to conventional methods. This approach provides a more reliable and efficient framework for managing fault events in next-generation smart grids. © 2024 IEEE.

关键词： Prediction models

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Hybrid Deep Learning Models for Hand Gesture Recognition with EMG Signals

Hybrid Deep Learning Models for Hand Gesture Recognition wit...

引用

2024 international conference on advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Neenu, R. Titus, Geevarghese Computer Science & Engineering Amal Jyothi College Of Engineering Apj Abdul Kalam Technological University Kerala Kanjirappally India Amal Jyothi College Of Engineering Department Of Electronics And Communication Engineering Kerala Koovappally India

ISBN: (纸本)9798350371567

Hand Gesture detection utilizing Electromyography (EMG) signals is a dynamic field of study with a wide range of applications, including human-machine interfaces and prosthetic devices. In recent years, several deep-learning algorithms have been created to classify EMG data. This paper examines the impact of hybrid models, which integrate CNN and RNN models, on hand gesture categorization using EMG signals. A comparative analysis is conducted on the performance of three hybrid models: CNN-GRU, CNN-LSTM, and CNN-BiLSTM. The performance assessment is conducted on the Ninapro DB1 dataset. The findings demonstrate that CNN-BiLSTM models outperform the other three models in terms of classification accuracy, achieving a superior result of 79.38%. But when the complexity of the model is a concern, CNN-GRU models may be a better choice. © 2024 IEEE.

关键词： Long short-term memory

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Effective Disease Recognition in Cucumbers: A Web-Based Application Using Transfer Learning Models 3

Effective Disease Recognition in Cucumbers: A Web-Based Appl...

引用

3rd IEEE international conference on robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Rahman, Md Shahidur Ahamed, Asif Pranto, Md Nuruzzaman Islam, Md Ariful Al Masum, Abdullah Al-Sakib, Abdullah Rahman, S.M. Arafat Haque, Rezaul Rahman, Shafiur Westcliff University Dept. of Engineering Management CA92614 United States Westcliff University College of Technology and Engineering CA92614 United States International American University Dept. of Business Administration CA90010 United States Westcliff University Dept. of Information Technology CA92614 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Cucumber farming plays a crucial role in Bangladesh's agricultural economy, significantly contributing to vegetable production. However, diseases like Downy Mildew, Bacterial Wilt, Anthracnose, and Belly Rot threaten crop quality and lead to financial losses. Effective management of these diseases is crucial for maintaining high crop yields and ensuring economic stability. Current disease identification methods are labor-intensive, costly, and dependent on expert knowledge. To address these challenges, this study presents an automated disease categorization system using advanced transfer learning models, including MobileNet, ShuffleNet, InceptionResNet-v2, AlexNet, and Ensemble Transfer Learning (ETL). Utilizing a dataset of 8,000 well-annotated images, enhanced through preprocessing techniques like GLRM feature extraction and InfoMAX-GAN feature selection, the ETL model achieved a remarkable accuracy of 99.79% and an F1-score of 99.78%. We also developed a user-friendly web tool for real-time diagnostics, providing invaluable support to farmers and agricultural specialists. This tool enables rapid disease detection, leading to increased productivity and a transformative approach to cucumber disease management. By reducing the reliance on chemical interventions, it promotes sustainable farming practices and safeguards human and animal health. © 2024 IEEE.

关键词： Feature Selection

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Multi-target trapping method of swarm robots based on mean-shift 6

Multi-target trapping method of swarm robots based on mean-s...

引用

6th international conference on robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Wang, Xiaoning Qin, Kun Yin, Weijie Hao, Chongqing School of Electrical Engineering Hebei University of Science and Technology Shijiazhuang China

ISBN: (纸本)9798331541699

The collective behavior of many creatures in nature serves as an inspiration for swarm robots. Therefore, this article proposes a mean-shift-based multi-target trapping method, which can enable swarm robots of different scales to accomplish the trapping task of targets in different distribution situations when facing multiple moving targets. And by combining the grouping mechanism proposed in this article, uniform allocation of swarm robots can be realized, which can effectively reduce the escape rate of targets. The effectiveness and feasibility of the method are validated through simulations experiments. © 2024 IEEE.

关键词： Microrobots

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Enhancing Face Mask Detection Using Data Augmentation Techniques

Enhancing Face Mask Detection Using Data Augmentation Techni...

引用

2023 international conference on Recent advances in science and engineering technology, ICRASET 2023

作者： Kumar, P. Vinodh Kumar, S. Priya, L. Senthilpandi, S. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350306927

In the ongoing pandemic that we are in general confronting, a cover has turned into a significant extra that we want to wear at whatever point we step out to safeguard ourselves and everyone around us. People who wear masks are to blame for the success of every nation that escaped the pandemic and resumed normalcy. Great reconnaissance estimates that guarantee that the general population is wearing covers consistently can assist us in beating this pandemic. Our existing camera systems can be used by computer vision systems to examine enclosed spaces and recognise individuals without masks. Best-in-class Item Recognition calculations can run at high derivation speeds, which will make this framework basically deployable progressively. Subsequently, further developing the acknowledgment execution of the current face acknowledgment innovation on the veiled faces is exceptionally critical. The latest high-level face acknowledgment approaches are planned in view of profound realisations, which rely upon countless face tests. The training of object detection models that are capable of locating both the unmasked and masked faces in an image is the objective of this paper. These indicators will likewise be hearty to commotion and give as little room as conceivable to oblige bogus up-sides for veils because of the possibly desperate results that they would prompt. © 2023 IEEE.

关键词： Object detection

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Industrial IoT Systems for Real-Time Environmental Monitoring

Industrial IoT Systems for Real-Time Environmental Monitorin...

引用

2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Seth, Jyoti Dutta, Abhishek Desai, Karishma Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India Atlas SkillTech University Department of UGDX Mumbai India

ISBN: (纸本)9798350388800

Innovative technology adoption is crucial for sustainable industrial practices in today's dynamic business climate. EcoSense360 is a revolutionary Industrial Internet of Things (IIoT) system for continuous environmental monitoring in manufacturing facilities, and it is presented in this article. Real-time anomaly detection, predictive maintenance, and energy optimization are the three cornerstone algorithms that make up EcoSense360. As the foundation of EcoSense360, the Real-Time Anomaly Detection Algorithm enables for instantaneous detection of environmental aberrations. The potential for environmental catastrophes is reduced, and crucial concerns may be addressed promptly, thanks to these real-time capabilities. Our testing shows that as compared to conventional approaches, EcoSense360 provides superior outcomes in terms of both data precision and real-time responsiveness, as well as regulatory compliance. EcoSense360 is a proactive and all-encompassing solution for environmental monitoring and management. Real-time environmental insights, predictive maintenance, and sustainable energy use are just a few of the ways in which its algorithms are changing the face of industry. Analysis shows EcoSense360 is superior to conventional approaches to environmental protection, resource conservation, and meeting sustainability targets. EcoSense360 is the best option for businesses that are serious about being environmentally responsible as they face increasing pressure to do so. © 2024 IEEE.

关键词： Environmental monitoring

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Research on Mobile Robot Path Planning Based on Meta-DQN Algorithm 4

Research on Mobile Robot Path Planning Based on Meta-DQN Alg...

引用

4th international conference on Artificial Intelligence, robotics, and Communication, ICAIRC 2024

作者： Liu, Cheng Lei, Fan Zhou, Hui Liu, Zhiwei Shaanxi University of Science & Technology School of Electrical and Control Engineering Shaanxi Xi'an China Shandong University of Technology School of Electrical and Electronic Engineering Zibo255000 China China Academy of Aerospace Science and Innovation Beijing China

ISBN: (纸本)9798331531225

Currently, artificial intelligence shows vigorous development. Mobile robot path planning, as a fundamental research topic in the field of artificial intelligence, is constantly undergoing iterative upgrades every year. DQN, as a traditional algorithm for deep reinforcement learning, has many limitations: the training process is trial and error by interacting with the environment intermittently, but every time it finishes training, the model needs to start from scratch to train the next task, which results in low-quality samples, long training time, and poor robustness. To solve the above problems, this study adds meta-learning on the basis of the traditional DQN algorithm to form a meta-depth reinforcement learning improvement algorithm, which takes advantage of their strengths and complements their weaknesses, utilizes the ability of meta-learning to adapt to the new task quickly, and fine-tunes the model through meta-knowledge so that the model itself obtains an autonomous learning mechanism, which greatly reduces the time to train the model. The environment was set as a raster map, the starting point, end point, and roadblock were determined, and the simulation was carried out on the MATLAB platform. The results show that the improved algorithm possesses the advantages of reducing the frequency of interaction with the environment, arriving at the goal point smoothly, and taking a shorter time compared with the traditional DQN algorithm. © 2024 IEEE.

关键词： MATLAB

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Exploring Reinforcement Learning Algorithms for Autonomous Agents in Dynamic Environments

Exploring Reinforcement Learning Algorithms for Autonomous A...

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2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Kumar, S. Magesh Swaminathan, Subbiah Sugumaran, D. Saranya, N. Naga Ashokkumar, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Information Technology Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Vel Nagar Tamil Nadu Chennai India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350388602

Reinforcement Learning (RL) algorithms are widely considered to be the enabling technology for deep learning and allow for agents in complex systems to learn in a dynamic environment. In order to present in this paper a break-through training methodology for learning RL agents in the dynamic environment, advised by the following adaptability, robustness, and scalability purposes. Our approach is assessed in an array of the model environments which are open to the users. This framework is labelled as OpenAI Gym environment. We apply different reinforcement learning algorithms in our experiments among which Covering Concurrent and Differentiable in Q-learning, Meta-learning (MAML), Virtual Imitation and Q-learning, and Dyna-Q are the most popular. Meta-learning's performance highlights its power and efficiency in grasping and optimizing decision-making logic in dynamic situations within 20 epochs and reaching the cumulative reward of 500. As a matter of fact, adaptive search strategies, for instance, UCB are another tool that is extremely useful reaching to 490 at 25 epochs after all. Transfer learning techniques like the Progressive Neural Networks shows extremely high power of learning in collecting a reward of 500 and in less than 20 epochs it also converges. Moreover, scalable parallel training methods such as CPDG which are normalized in distribution, have huge scalability and robustness can be applied for large scale dynamic environments. Consequently, it is concluded that the presented approach was very good in giving RL agents the ability to navigate environments and succeed in dynamic settings, thus contributing to the development of autonomous systems not only in the area of RL but also everywhere else. © 2024 IEEE.

关键词： Adversarial machine learning

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A Deep Learning Based Critical Analysis Of Skin Lesion Classification

A Deep Learning Based Critical Analysis Of Skin Lesion Class...

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2024 international conference on advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Rahul Gupta, Meenu Kumar, Rakesh Obaid, Ahmed J. Chandigarh University Dept. Of Cse Punjab India University Of Kufa Faculty Of Computer Science And Mathematics Najaf Iraq College Of Technical Engineering National University Of Science And Technology Dhi Qar Iraq

ISBN: (纸本)9798350371567

In everyday life, various exposures at work or at home can lead to skin conditions such as allergies or infections. Skin lesions serve as essential indications, alerting to future issues and requiring immediate care and attention. To search for publications concerning skin lesions and diverse procedures, the most well-known scientific databases, such as web of science, scopus, and IEEE Xplore, were used. To find comparable documents, search engines use keywords such as 'skin lesion,' 'CNN,' 'skin diseases,' and so on. Following a comprehensive screening method, 658 abstracts from databases are collected, and 19 full-text articles are identified for consideration. This paper investigates the profound impact of machine learning in dermatology, with a focus on skin lesion classification. This study provides a critical examination of CNN-based models, emphasizing their importance and difficulties in increasing precision diagnosis and treatment in dermatology. Overall, this study provides helpful insights into various approaches for skin lesion classification. © 2024 IEEE.

关键词： Dermatology

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