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Real-Time Snake Detection with Alert systems using Deep learning 15

Real-Time Snake Detection with Alert Systems using Deep Lear...

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15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Raju, Jyothika K. Maitri, V.J. Hemavathy, R. Ramakanth Kumar, P. Shankar, T. R.V.College of Engineering Bengaluru India CCTV Research Center R.V.College of Engineering Bengaluru India

ISBN: (纸本)9798331300579

Snake interactions can pose serious dangers to public safety and biodiversity preservation in both human-populated areas and animal environments. The creation of a Snake Detection From Video Footage and Alert System utilizing AI and ML technologies has attracted interest as a solution to this problem. The system attempts to reliably identify snakes and issue timely alerts, reducing human-snake conflicts and improving coexistence. It does this by utilizing real-time video processing, object detection techniques like YOLO, and intelligent decision-making. Current studies and projects in this area have shown that it is possible to identify snakes in video footage using machine learning techniques. Many researchers have used deep learning techniques, such as YOLO, to identify snakes in real time and with accuracy. The ability of contemporary systems to adapt to various situations, nevertheless, is still a study area that needs further attention. Real-time processing requirements may also encounter difficulties in contexts with limited resources. This gap will be filled by the proposed project's adaptive Real-Time Snake Detection with Alert systems Using Deep learning. Real-time snake recognition and alarm creation from a model's video feed are among the goals. With the help of these goals, the project hopes to provide a thorough and useful solution that will aid in the protection of both humans and wildlife in areas where snakes are a common occurrence. © Grenze Scientific Society, 2024.

关键词： Biodiversity

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Advancements in Immunization Management for Personalized Vaccine Scheduling with IoT and machine learning 10

Advancements in Immunization Management for Personalized Vac...

引用

10th international conference on Communication and Signal Processing, ICCSP 2024

作者： Madheswaran, M. Dhanalakshmi, R. Ramasubramanian, G. Aghalya, S. Raju, S. Thirumaraiselvan, P. Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Information Technology Tamil Nadu Namakkal India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Immunization is essential to public health;currently, comprehensive vaccine schedules may not sufficiently address individual health profiles. The Internet of Things (IoT) collects real-time health data, and decision trees (DTs) develop personalized vaccination schedules. Wearable health monitors and electronic health records provide continuous health data and information. DTs are customizable machine learning (ML) algorithms that assess this data and create immunization regimens. Based on age, vaccination history, underlying health issues, and real-time health measurements, DTs provides individualized vaccine recommendations. More vaccination compliance and greater health needs alignment are the system's main benefits. Based on real-time data, this technique recommends vaccination administration timings. Optimization of vaccination systems reduces vaccine-preventable illness transmission. Integrating IoT data, preprocessing, and vaccination scheduling using DTs are covered in this work. It emphasizes data security and ethics while managing sensitive health information during privacy and security issues. Personal vaccination scheduling using IoT and DTs advances public health. Regulations that protect privacy and provide equal access to personalized vaccination schedules are essential as the system matures. This novel method might save lives, save healthcare costs, and improve vaccination systems. © 2024 IEEE.

关键词： Vaccines

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Applications of machine learning Methods for Positioning in Scanning Electrochemical Microscope 28th

Applications of Machine Learning Methods for Positioning in ...

引用

2024 international conference Automation

作者： Ivinskij, Vadimas Pandey, Vijitashwa Morkvenaite-Vilkonciene, Inga Vilnius Gediminas Tech Univ LT-10223 Vilnius Lithuania Oakland Univ Rochester MI 48309 USA State Res Inst Ctr Phys Sci & Technol Vilnius Lithuania

ISBN: (纸本)9783031782657;9783031782664

A typical scanning electrochemical microscope (SECM) consists of a three-axis positioning system, usually with stepper motors and potentiostat. Working at a micro and nanoscale, the most consistent problem is vibrations, which an antivibration table can solve. However, this is not a solution for some devices placed in small environments. Therefore, we suggest reducing vibrations by machine learning methods, making SECM more modular. In this paper, we compare and evaluate the use of neural networks within similar systems that have specialized architecture requirements, the use of samples that require a specialized approach to attribute extraction concerning feature fidelity for learning algorithms, control-machine learning combination methods for static and dynamic applications in electrochemical systems as well as application of neural network functions as stand-alone packages. The results were taken for the application of prototyping a tangible and portable electrochemical scanning device system. Potential improvements, such as micro stepping instead of time delay between steps, are considered. The proposed methods in this paper are aimed at countering the disturbances caused by transient processes during actuation and control tuning using non-evasive methods for cheaper solution alternatives.

关键词： Scanning electrochemical systems machine learning Control systems

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A new attacks intrusion detection model based on deep learning in Software-Defined Networking Environments 4

A new attacks intrusion detection model based on deep learni...

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4th international conference on machine learning and Intelligent systems engineering (MLISE)

作者： Yang, Jikun Univ Melbourne Sch Comp & Informat Syst Melbourne Vic Australia

ISBN: (纸本)9798350375084;9798350375077

Software-defined networking (SDN) has transformed the landscape of network communication. SDN separates the control plane from the data plane, offering a centralized management system and dynamic resource allocation. Nevertheless, SDN is susceptible to security risks, necessitating the deployment of sophisticated Intrusion Detection systems (IDS). Several researchers have recently employed machine learning and other cutting-edge technologies to analyze and identify rapidly growing attacks and anomalies. However, the majority of these techniques exhibit low accuracy and poor scalability. In response to this challenge, this paper proposes an Intrusion Detection System (IDS) framework based on the Convolutional Neural Network-Gated Recurrent Unit (CNN- GRU) network. This framework leverages Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to identify real-time network intrusions. The framework was trained and evaluated on the UNSW-NB15 and InSDN datasets using Bayesian Optimization (BO), achieving exceptional accuracy and F1 scores exceeding 99.93% on the UNSW-NB15 dataset. Similarly, on the InSDN dataset, the framework achieved an accuracy of 99.93%, with precision, recall, and F1 score values of 99.89%, 99.97%, and 99.93%, respectively. These demonstrate the framework's effectiveness in discerning between normal and malicious network behavior.

关键词： SDN IDS CNN-GRU

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Identification of Fake Medicinal Raw Materials using machine learning Techniques 11

Identification of Fake Medicinal Raw Materials using Machine...

引用

11th IEEE international conference on advances in Computing and Communications, ICACC 2024

作者： Raghi, R.K. Hariharaan, S. Sudha, K. St. Joseph's College of Engineering Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379136

This paper extends the methodology for leaf classification by utilizing ResNet50, particle swarm optimization for hyperparameter tuning (PSO), and support vector machines (SVM) to classify 30 species, compared to the previous work that classified 7 species. The proposed approach pulls down the powerful feature extraction capabilities of ResNet50, optimized through PSO for hyperparameter tuning, and classified with SVM. By incorporating these advanced techniques, our approach achieves a high accuracy of 99.51%, which significantly outperforms previous methods. The scalability and robustness of this combined model are thoroughly demonstrated through extensive experimentation and evaluation on a comprehensive dataset of over 3500 leaf images. The results highlight significant improvements in classification performance, showing the model's effectiveness in handling a larger and more diverse set of species. This advancement underscores the potential for broader applications in various fields, including botanical studies, agricultural automation, and environmental monitoring. By providing a scalable solution that maintains high accuracy, our methodology can be utilized for large-scale plant identification and monitoring systems, contributing to more efficient agricultural practices and better understanding of plant biodiversity. The promising results set a new benchmark for research and development in the domain of leaf classification in future, encouraging further exploration of deep learning techniques and optimization methods to enrich classification accuracy and applicability across different plant species and environmental conditions. © 2024 IEEE.

关键词： Adversarial machine learning

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Multi-Behavioral Recommender System Based on Multi-View Contrastive learning 4

Multi-Behavioral Recommender System Based on Multi-View Cont...

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4th international conference on machine learning and Intelligent systems engineering (MLISE)

作者： Zhang, Haiyang Gao, Rong Liu, Donghua Wan, Xiang Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China China Waterborne Transport Res Inst Beijing Peoples R China Wuhan Second Ship Design & Res Inst Wuhan Peoples R China

ISBN: (纸本)9798350375084;9798350375077

In modern recommendation systems, diverse user behavior data such as browsing, clicking, and purchasing provide rich information for personalized recommendations. However, effectively integrating and utilizing these varied behavioral data remains a challenge. This paper proposes a multi-behavior recommendation approach based on multi-behavior and contrastive learning. Firstly, multiple user and item views are generated through different masking mechanisms to capture diverse user behavioral characteristics. Subsequently, the LightGCN model is employed to generate embedding representations for users and items, effectively learning the interaction information between them. Next, leveraging contrastive learning methods for the same behaviors across different views involves pulling embedding vectors of similar behaviors closer while pushing those of dissimilar behaviors farther apart, thereby enhancing the model's discriminative power. Finally, aggregation of multiple behavior views and optimization using the Bayesian Personalized Ranking (BPR) loss function aim to maximize ranking differences, further improving recommendation accuracy. Experimental results demonstrate that the proposed approach effectively leverages diverse user behavior data, significantly outperforming traditional single-view and non-contrastive learning-based recommendation methods in terms of recommendation precision and user satisfaction.

关键词： multi-behavior view generation contrastive learning recommendation systems

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Enhancing Crypto Transaction Security: A machine learning Approach 10

Enhancing Crypto Transaction Security: A Machine Learning Ap...

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10th international conference on Electrical Energy systems, ICEES 2024

作者： Mekala Rani, T.M.S. Suresh, A. Bhargavi, S. Vardhan Reddy, M. Harsha Sai Nikhil, K. Priya, G. Chandu Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh India Andhra Pradesh India

ISBN: (纸本)9798350353778

The project aims to enhance the security of cryptocurrency transactions through the implementation of advanced machine learning methodologies that are RNN(recurrent neural networks),LSTM,VGG16. We aim to create a strong and flexible system that can recognize and stop fraudulent activity in real-time by utilizing machine learning techniques. By utilizing dataset containing the features related to average time between sent and recived transactions, amount involved. This project focuses on creating a proactive defense mechanism that can analyze transaction patterns, detect anomalies, and promptly alert users or intervene to mitigate potential threats. By combining cutting-edge machine learning techniques with the ever-evolving landscape of cryptocurrency security, this project aims to safeguard digital assets and promote trust in the rapidly growing crypto ecosystem. © 2024 IEEE.

关键词： Adversarial machine learning

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advances in ECG Technology and Instrumentation 1

Advances in ECG Technology and Instrumentation

引用

1st international conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Panchbudhe, Dhanshree Phutane, Prasanna Department of Pharmaceutics Wardha India

ISBN: (纸本)9798350360707

In the field of cardiology, electrocardiography (ECG) is an essential diagnostic technology that offers crucial information on the cardiac electrophysiological activity. Significant developments in ECG apparatus and technology throughout time have completely changed how cardiac data is gathered, processed, and evaluated. An summary of the major advancements in ECG apparatus and technology is given in this abstract, along with a discussion of how they affect patient care and clinical practice. Better Hardware: Wearable, portable ECG systems that may be monitored remotely are made possible by improved electrode, lead, and sensor combinations that improve signal quality. Enhanced Signal Processing: Artificial Intelligence and machine learning make it easier to read ECGs, identify minor problems, and help determine risk. © 2024 IEEE.

关键词： Electrocardiography

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Adaptive Kernel Optimization for Probabilistic learning: Integrating Support Vector machines with Gaussian Process Frameworks 1

Adaptive Kernel Optimization for Probabilistic Learning: Int...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Kanth, Tatiraju V. Rajani Singh, Balveer Singh, Yaspal Bhutada, Sunil Yamaganti, Rohita P.K. University Department of Computer Science Engineering Shivpuri India P.K. University Department of Computer Science Engineering Shivpuri India P.K. University Department of Information Science Department of Computer Science Engineering Shivpuri India

ISBN: (纸本)9798331529246

With its robust capabilities for non-linear regression and classification, kernel-based learning has emerged as a fundamental component of state-of-the-art machine learning approaches. In order to improve probabilistic learning, this study investigates Adaptive Kernel Optimization (AKO), a new method that combines the best features of the Support Vector machine (SVM) and the Gaussian Process (GP) frameworks. Achieving better flexibility in modeling complicated data distributions while keeping computational efficiency is achieved by employing adaptive kernel functions in the suggested strategy. Quantifying uncertainty in addition to deterministic SVM classifications is made possible with the incorporation of GP kernels, which offer probabilistic insights. The suggested approach guarantees resilience across varied and high-dimensional datasets by dynamically adjusting kernel parameters according to data properties. Extensive testing on benchmark datasets shows that, in comparison to conventional SVM and GP approaches, our model generalizability, classification accuracy, and interpretability are much improved. Autonomous systems, healthcare diagnostics, and financial sectors can all benefit from the scalable, adaptive, and probabilistic learning models that this study establishes. © 2025 IEEE.

关键词： Adversarial machine learning

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Development of a Multimodal machine learning Model for Seizure Detection Using Wearable Devices 1

Development of a Multimodal Machine Learning Model for Seizu...

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1st IEEE international conference on advances in Computing, Communication and Networking, ICAC2N 2024

作者： Singh Rathore, Saurabh Pratap Magotra, Sumedha Sruthy, K.V. Sharma, Sonu Kumar Kaur, Gaganpreet Singh, Sailendra ICAPSR Dept. of Management New Delhi India Chandigarh University Dept. of Computer Science Engineering Chandigarh India Dhanalakshmi Srinivasan College Dept. of Computer Science & Engineering Coimbatore India Mangalayatan University Dept. of Comp. Eng & Applications Aligarh India Lovely Professional University Punjab India J.S. University Dept. of Computer Science Shikohabad India

ISBN: (纸本)9798350356816

This research explores the transformative potential of wearable devices designed to automatically detect & predict epileptic seizures, offering continuous monitoring & early detection capabilities. The study focuses on developing a machine learning model tailored for seizure detection in such devices, leveraging multimodal sensors such as Electrodermal Activity (EDA) & Accelerometer (ACC). By measuring skin resistance and identifying irregular heartbeats, which are signs of upcoming seizures, these sensors allow for precise seizure detection. The study intends to demonstrate the effectiveness of this machine learning model in wearable technology with the goal of improving patient outcomes and seizure management. © 2024 IEEE.

关键词： Accelerometers

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