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Real-Time Detection of Sea Urchins Using Convolutional Neural Networks

Real-Time Detection of Sea Urchins Using Convolutional Neura...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Nair, Vismaya V Bolem, Srinivas Nanthini, N. Parveen Akhther, A. Sathyabama University Dept. of Computer Science Engineering Chennai India National Institute of Ocean Technology Chennai India

ISBN: (纸本)9798331540821

The marine ecosystem contributes significantly to our planet's ecological balance. It supports a wide range of organisms, from microscopic plankton to giant marine mammals. Each species plays a unique role in the proper functioning of the ecosystem. One such species that significantly contributes to the biodiversity of the marine environment is the Sea urchins. The Sea urchins are known as the' indicator species' of the marine ecosystem and their presence provides valuable insights on the health and overall condition of the marine environment. Monitoring, understanding, and analyzing the population growth of sea urchins is vital for research and conservation. There has been multiple research that focus on identification and classification of other marine species but only a few have ventured into the domain of sea urchin detection. This paper presents an approachto automate the detection of sea urchins on the ocean floor using Convolutional Neural Networks (CNNs), computer vision and image processing. By harnessing CNN, we aim to simplify the labor-intensive task of manually identifying sea urchins, which are still largely carried out by divers. The proposed approach aims to provide a robust solution for the detection of sea urchins, which in turn would help in enhancing marine research and conservation techniques. Additionally, the model is loaded into Raspberry Pi, which is placed on a surveillance vehicle or aplatform above the surface of the water, it processes the input received from the camera that is deployed underwater in a watertight casing. The results are then transmitted to the user via GSM model of communication to any device within its vicinity. Our model shows an accuracy rate of 95.31%. By automating the process, this model facilitates the study of sea urchins and the surrounding marine environment, making it more accessible. This method helps to close the gap between marine biology, computer vision and deep learning. © 2024 IEEE.

关键词： Convolutional neural networks

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Nutrition Estimation and Dietary Assessment in Indian Food through Deep Learning

Nutrition Estimation and Dietary Assessment in Indian Food t...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Nandanwar, Amit Kumar Kushwaha, Mohit Vishwatej, Kondabathula Singh, Dhirendra Pratap Choudhary, Jaytrilok Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering M.P Bhopal India

ISBN: (纸本)9798350348460

Food Nutrition prediction is an interesting area of work before 10 years. There was a active research went on. But after that there was a break of research on this area. Now after corona some active work is going on. As the people are interested to know the intake of their food nutrition. And in this paper proposing the nutrition prediction in the Indian food. In this paper followed the deep learning approach to predict the nutrition in the food. The person who is interested in finding the nutrition intake will capture the image of the food before intake and can get the nutrition values before going to consume. So, it can help the people to get an idea and track of the nutrition they are *** this we are using a deep learning approach to find the nutrition in the image. User will capture the food image then that image is sent through CNN model. then by the CNN model will find what food item is captured. And before capturing the image will ask user information also like name, age, height and weight. So will calculate the BMI and the nutrition required for the person. And will store the nutrition content user is consuming in every meal. So, at end will show the BMI of the user, and present nutrition user is consuming from the meal and total nutrition user has to consume in a day. Our model for food classification achieved an accuracy of 91.87% © 2024 IEEE.

关键词： BMI (Body Mass Index) Convolutional Neural Networks Data collection Deep Learning Dietary management Keras Machine Learning Pretrained models TensorFlow Transfer Learning

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Anomaly Detection from Web Log Data Using Machine Learning Model 7

Anomaly Detection from Web Log Data Using Machine Learning M...

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7th International Conference on computer Applications in Electrical engineering-Recent Advances, CERA 2023

作者： Mishra, Amit Kumar Bagla, Piyush Sharma, Ravi Pandey, Neeraj Kumar Tripathi, Neha Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India National Institute of Technology Department of Computer Science & Engineering Jalandhar India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350305005

The information in the logs produced by the servers, devices, and applications can be utilized to assess the system's health. It's crucial to manually review logs, for instance, during upgrades, to verify whether the update and data movement went smoothly. Manual testing is insufficiently trustworthy, and manual log examination takes much time and effort. In this paper, we propose to search log files for anomalous sequences using the machine learning methods K-Means and DBSCAN. The two data representation approaches examined in this study were feature vector representation and IDF representation. The effectiveness of the deployed machine learning algorithms was examined using evaluation measures like F1 score, recall, and precision. The study found considerable differences in the algorithms' capacities to spot anomalies, with some algorithms being better at seeing various types of abnormal arrangements than their overall prevalence. By using the study's findings, the user might be able to spot strange arrangements after manually sifting through the log file. © 2023 IEEE.

关键词： Anomaly detection

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Power and Area Optimized Memristor-Based Majority Identifier for Digital Applications 3

Power and Area Optimized Memristor-Based Majority Identifier...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Raj, P. Michael Preetam Vittapu, Sravan K. Chinnasamy, P. Gupta, Manu Samatha, K. Sasivijaya, K. Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Nalla Narasimha Reddy Education Society's Group of Institutions Department of Electronics and Communication Engineering Hyderabad India Kalasalingam Academy of Research and Education Dept. of Computer Science and Engineering Tamil Nadu India Sreenidhi Institute of Science and Technology Department of Electronics and Computer Engineering Hyderabad India

ISBN: (纸本)9798331518592

In earlier developments, CMOS technology played a great role in realizing majority identifiers. There are huge drawbacks that affect the circuit's performance. To overcome the drawbacks of CMOS-based majority Identifiers, novel memristor-based majority and minority Identifiers have been proposed in this work. The features offered by memristor have motivated us to use the component and achieve greatly improved results compared to CMOS-based majority identifier circuits. After observing the behavior of the basic memristor-based majority identifier circuit, we have decided to design and implement a memristor-based majority and minority identifier circuit which can be used in many automation systems to increase quality and productivity. © 2024 IEEE.

关键词： CMOS integrated circuits

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Multi-Access Edge Computing Using Intelligent Mobile User Resource Allocation In 6G 4

Multi-Access Edge Computing Using Intelligent Mobile User Re...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Saeed, Mamoon M. Saeed, Rashid A. Mokhtar, Rania A. Gaid, AbdulGuddoos S. A. Ahmed, Zeinab E. Elbasheir, Mohammed S. Faculty of Engineering Department of Communications and Electronics Engineering Yemen Taif University Faculty of Engineering Department of Electrical and Electronic Engineering Altaif Saudi Arabia College of Computers and Information Technology Taif University Department of Computer Engineering Taif21944 Saudi Arabia Taiz University Faculty of Engineering & Information Technology Dept. of Communication & Computer Engineering Taiz Yemen International Islamic University Malaysia Department of Electrical and Computer Malaysia School of Electronic Engineering Sudan University of Science and Technology Khartoum Sudan

ISBN: (纸本)9798350372632

Edge computing plays a crucial role in the advancement of Beyond 5G (B5G) and 6G networks. The substantial growth in network traffic and computational requirements necessitates the implementation of multi-access edge computing (MEC) as a viable solution. MEC aims to bring the capabilities of cloud computing closer to end-users by integrating them into the radio access network (RAN). This research introduces an optimal approach to connect mobile users with MEC hosts and access points, effectively distributing radio and computing resources to individual mobile users. The objective is achieved by lowering the total user transmit power while abiding by latency constraints in both the communication and computation phases, using the Grey Wolf Optimization technique (GWO). A comparison to alternative smart techniques demonstrates the effectiveness of the suggested approach © 2024 IEEE.

关键词： 5G mobile communication systems

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Predictive Cloud-Based Application for the Early Detection and Risk Stratification of Oral Cancer using Keratin Pearls, Multiple Nucleoli and Vascular Invasion 28

Predictive Cloud-Based Application for the Early Detection a...

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28th International computer Science and engineering Conference, ICSEC 2024

作者： Benagi, Abhinav Narayan, Dhanyatha Priya, K. Shetty, Savita K Augustine, Dominic Sv, Sowmya Kanavalli, Anita Rage, Charith Reddy, A Susmitha Ramaiah Institute of Technology Dept of Computer Science and Engineering Bangalore India Ramaiah Institute of Technology Dept of Information Science and Engineering Bangalore India MS Ramaiah Dental College and Hospital Dept of Oral Pathology and Microbiology Bangalore India

ISBN: (纸本)9798350366860

Oral cancer is a significant public health concern, with high morbidity and mortality rates. Early detection is critical for improving patient outcomes and reducing the burden of the disease. Histopathological images, essential for distinguishing between normal, oral precancerous, and cancerous lesions, may miss critical diagnostic features of aggressiveness. Additionally, routine histopathology struggles to effectively identify high-risk OSCC patients. This work was conducted in collaboration with Ramaiah Institute of technology and Ramaiah Dental College to improve OSCC detection through a pipeline-based computer-Assisted screening approach involving classification, feature detection, and risk stratification. First, the classification module achieved an accuracy of 99.25% with EfficientNet in classifying cancerous and non-cancerous lesions. The feature detection module successfully identified key indicators of aggressiveness, including keratin pearls and multiple nucleoli using YOLO-NAS, while vascular invasion was detected by the EfficientNet classifier. Finally, we stratified cancer risk based on these detected features, producing a comprehensive pathology report that provides clinicians with valuable insights for informed diagnosis. Ultimately, this work aims to provide doctors with insights from analyses using models trained on key features like keratin pearls, multiple nucleoli, and vascular invasion. © 2024 IEEE.

关键词： Lung cancer

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Real-Time Helmet Detection System with Vehicle Number Extraction for Two-Wheeler Vehicles using YOLOv8 2

Real-Time Helmet Detection System with Vehicle Number Extrac...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Kumar, Abhash Guru Prasad, M.S. Soni, Dheeraj Rani, Vibha Arora, Kanika Ganesh, D.R. Dept. of Computer Science & Engineering Dehradun India REVA University School of Computing and Information Technology Bengaluru India

ISBN: (纸本)9798350371314

In the face of increasing two-wheeler usage and the persistent issue of riders neglecting helmet compliance, this paper proposes a groundbreaking Real-Time Helmet Detection System with Vehicle Number Extraction. The existing literature on helmet detection systems is reviewed, revealing a range of methodologies, accuracies, and limitations in current approaches. Building upon this foundation, the three-stage detector combines the efficiency of YOLO (You Only Look Once) for object detection with a specialized number plate extraction system. The system aims to provide real-time detection of two-wheeler riders, classification of helmet usage, and extraction of vehicle numbers. The proposed methodology, utilizing a pre-trained YOLO model for object detection and EasyOCR library, demonstrates an accuracy rate of 64% for vehicle detection, 78% for helmet detection, and 92% for number plate extraction. The practical implementation of this system showcases its potential for enhancing road safety, automating traffic management, and promoting regulatory enforcement. While showing potential, the ongoing research and development face challenges such as limitation of computational resources and privacy considerations. The Real-Time Helmet Detection System stands as a promising solution for improving road safety, with the potential for broader use in traffic management and safety enforcement. Continuous improvement and adaptation will guarantee its effectiveness in contributing to a safer and more controlled traffic environment. © 2024 IEEE.

关键词： Accident prevention

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Auto Disturbance Rejection Control Based on A Neural Network Predictive State Observer 15

Auto Disturbance Rejection Control Based on A Neural Network...

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15th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2023

作者： Du, Shengzhi Van Wyk, B.J. Nahri, N.F.S. Tu, Chunling Tshwane University of Technology Dept. Electrical Engineering Pretoria South Africa Tshwane University of Technology Dept. Computer Systems Engineering Pretoria South Africa

ISBN: (纸本)9798350326178

This paper addresses the auto disturbance rejection control (ADRC) for systems with time delay. ADRC is an effective control for most linear or nonlinear systems with noises and disturbances, because of its advantage on disturbance estimation and compensation. The extended state observer (ESO) is a critical component of ADRC. However, this observer is usually designed for systems without time delay. The time delay in a system could lead the ADRC unstable when the phase delay of the response cannot be ignored. This paper proposes a neural network based predictive state observer (NNPSO) to predict the response of systems with time delay. The simulation results validate the proposed method when systems with time delay are controlled. © 2023 IEEE.

关键词： Time delay

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An Energy Management Strategy in Hybrid Electric Vehicles: The Present and The Future Scenario 2

An Energy Management Strategy in Hybrid Electric Vehicles: T...

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2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

作者： Sabarish, G. Maria, Nivetha A Shanthini, M.K. Mayurappriyan, P.S. Ganesh, C. Shanmugasundaram, R. Dept of Electronics and Instrumentation Engineering Kumaraguru College of Technology Coimbatore India Dept of Computer and Communication Engineering Rajalakshmi Institute of Technology Chennai India Dept of Electrical and Electronics Engineering Sri Ramakrishna College of Engineering Coimbatore India

ISBN: (纸本)9798350306811

There has been a significant increase in interest towards "The electrification of transportation"in the past decade. The environmental crisis caused due to global warming and the depletion of oil supplies are factors that have made the automobile sector adopt strategies to produce eco-friendly, sustainable vehicles instead of their commercial fuel propelled vehicles. Most governments have expressed their interest in alleviating the environmental impact caused due to vehicle pollution by introducing various policies that eventually help people switch to Electric Vehicles (EV). Though there has been a surge of interest among people to adopt environmentally friendly, economically affordable electric vehicles, the major concern is the state of charge and range offered by these EVs. The global automobile sector has started to invest in research dedicated to the energy management system in EVs to overcome these issues. The emergence of artificial intelligence (AI) and the advancement of control systems are clear signs for developing smart-energy management systems in EV. This paper reviews the methods of energy storage and usage in EV following the various Energy Management Systems (EMS) present in EV such as Rule-based EMS and Optimization-based EMS and its types. Also, it gives an insight into the emergence of futuristic AI-based chips that would revolutionize the energy-management system in E-vehicles. © 2023 IEEE.

关键词： Neural networks

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Identification of Children with ADHD from EEG Signals Based on Entropy Measures and Support Vector Machine 11

Identification of Children with ADHD from EEG Signals Based ...

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11th European Workshop on Visual Information Processing, EUVIP 2023

作者： Maniruzzaman, Md. Al Mehedi Hasan, Md. Suzuki, Taro Shin, Jungpil The University of Aizu School of Computer Science and Engineering Aizuwakamatsu965-8580 Japan Rajshahi University of Engineering and Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350342185

Attention deficit hyperactivity disorder (ADHD) is one of the major psychiatric and neurodevelopment disorders that affects 11% of children worldwide. Moreover, the prevalence of ADHD has rapidly increased over time worldwide. According to DSM-V, three types of symptoms such as inattention, hyperactivity, and combined type (inattention with hyperactivity). So, it is necessary to use a simple, non-invasive, and automatic detection system for identifying children with ADHD at an early stage. The objective of this study was to propose a machine learning (ML)based ADHD-combine type (ADHD-CT) detection system from electroencephalogram (EEG) signals. EEG signals were recorded from nineteen ADHD-CT children and fourteen healthy children. We extracted five entropy-based features such as approximate-based entropy, Shanon-based entropy, permutation-based entropy, sample-based entropy, and singular value decomposition (SVD)-based entropy from each signal. The subset of the most informative and discriminative features was selected for ADHDCT using sequential forward floating selection (SFFS). Following that, support vector machine (SVM) was implemented with leave-one-out cross-validation for the identification of ADHD-CT children and assessed its performances based on classification accuracy. Our results illustrated that SVM with polynomial kernel provided 96.87% classification accuracy to discriminate children as ADHD-CT and healthy children. Our findings showed that our proposed system can be used to detect children with ADHD-CT. © 2023 IEEE.

关键词： Support vector machines

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