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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Rajarajeswari Veeramalai Sankaradass Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

This research explores how well Artificial Neural Networks (ANN) and Convolutional Neural Networks (NCNN) detect animal hide imperfections to improve quality of weather. Materials and Methods: The study extracted 3600 leather images from Kaggle and split them into two separate groups for this investigation. Group 1 used NCNN, and Group 2 used ANN. Statistical testing generated a sample size of 20 specimens at 95% confidence with 80% power in this experiment. Python became the chosen programming platform for data analysis and preprocessing. NCNN shows better results than ANN because it delivers 96.80% accuracy at 0.001 significance level (p<0.05). Conclusion: The analysis proves the Novel Convolutional Neural Network successfully finds defects in animal hides, but ANN cannot match its results.

关键词： Productivity Accuracy Data analysis Animals Statistical analysis Programming Convolutional neural networks Artificial intelligence Python Meteorology

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Optimization Effects of Rotating Transmitarray for Direction-of-Arrival Estimation with a Single Antenna

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IEICE Communications Express 2025年第6期14卷 210-213页

作者： Ryusuke Sunagawa Hibiki Shiiba Makoto Sano Ryuji Kuse Takeshi Fukusako Dept. of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

This paper discusses the optimization effects of a transmitarray on a direction-of-arrival (DOA) estimation using the transmitarray and a single receiving antenna. The transmitarray consists of 1-bit elements with 0° and 180° transmission phases. The transmission phase distribution was optimized using a genetic algorithm and compared to a random distribution. The optimized transmitarray achieves a DOA estimation accuracy comparable to that of a conventional system using a 3-element linear array antenna. The root mean square error (RMSE) of the optimized system exhibits a decreasing trend as the signal-to-noise ratio (SNR) increases. Notably, at an SNR of 20 dB, the RMSE is 20° lower than that of the 3-element linear array antenna.

关键词： Estimation Direction-of-arrival estimation Linear antenna arrays Accuracy Antenna radiation patterns Transmitting antennas Optimization Signal to noise ratio Receiving antennas Vectors

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Growth Rate Prediction on Fetal Brain Development using Deep Learning Techniques

Growth Rate Prediction on Fetal Brain Development using Deep...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Vellampalli Krishna Sai Mahesh Veldurthi Pavan Kumar Pandiselvam Pandiyarajan Vavila Raveendra Y. Mukesh Kumar Reddy Dept. of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India Dept. of Computer Science and Engineering Kalasalingam Academy of Research and Education Kalasalingam University Krishnankovil Tamilnadu India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

The development of the fetal brain is crucial for a child's cognitive and emotional maturation, highlighting the importance of early identification of neurological issues for effective prenatal care. This study introduces an innovative deep learning framework aimed at predicting fetal brain growth rates from 2D ultrasound images. We utilize a Convolutional Neural Network (CNN) that combines longitudinal data with various imaging modalities to enhance prediction accuracy. By incorporating transfer learning alongside sophisticated techniques like attention mechanisms, our model adept.y captures both spatial and temporal dynamics within the data. Results from our experiments demonstrate that this approach outperforms conventional methods, achieving an accuracy. The findings of this research hold significant promise for prenatal healthcare by enabling timely interventions for high-risk pregnancies. Ultimately, this study contributes to a deeper understanding of fetal brain development and illustrates the potential of integrating AI technologies into standard prenatal diagnostic practices.

关键词： Deep learning Pregnancy Accuracy Ultrasonic imaging Three-dimensional displays Transfer learning Imaging Brain modeling Data models Convolutional neural networks

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Advantages of MIMO Radar for Contactless Pulse Waveform Monitoring using End-to-End Learning

Advantages of MIMO Radar for Contactless Pulse Waveform Moni...

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IEEE Radio and Wireless Symposium (RWS formerly RAWCON)

作者： Chandler J. Bauder Aly E. Fathy Dept. of Electrical Engineering & Computer Science University of Tennessee Knoxville Knoxville TN USA

ISBN: (数字)9798350377743

ISBN: (纸本)9798350377750

Radar-based methods for contactless pulse monitoring typically use heart rate estimation accuracy as the primary performance evaluation metric. Recently, more in-dept. metrics such as heart rate variability (HRV) have been of interest, requiring precise pulse waveform reconstructions for reliable estimations. Traditional phase variation-based vital signs signal processing techniques have proven to be susceptible to sources of noise, impacting the shape of the extracted pulse waveform. In this work, the advantages of using raw multi-channel radar data and deep learning in an end-to-end manner for precise pulse waveform reconstruction are explored. Preliminary results show electrocardiogram (ECG) waveforms can be reconstructed from raw in-phase and quadrature 60 GHz multiple-input-multiple-output (MIMO) frequency modulated continuous wave (FMCW) radar data using an end-to-end neural network. Additionally, an ablation study is performed, revealing that waveform reconstruction similarity and estimation accuracy of several HRV metrics generally improve as the number of utilized MIMO channels increases.

关键词： Estimation error Accuracy Shape Channel estimation Radar Electrocardiography MIMO Telemetry Heart rate variability Monitoring

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Design and Investigation of 8×10Gbps WDM System using Different Modulators

Design and Investigation of 8×10Gbps WDM System using Diffe...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Kumari, Meet Sharma, Neha Chauhan, Rahul Joshi, Kireet Kumar, Amit UIE Chandigarh University Dept. of ECE Mohali140413 India Chitkara University Chitkara University Institute of Engineering and Technology Punjab Rajpura India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350361537

A high speed 8×10Gbps wavelength division multiplexing (WDM) system over long-haul fiber is designed and investigated. The system performance is measured for district optical modulators or varied fiber range and throughput. Simulation results depict that faithful fiber range of 55km can be obtained by incorporating single drive mach-zehnder modulator than electroabsorption and MZM modulators. Besides, the fiber range can be extended upto 120km at 5Gbps throughput. The obtained eye patterns are also depict the superior performance of the system for varied fiber range using single drive MZM. Besides, this work illustrates best performance as compared to existing works. © 2024 IEEE.

关键词： Electroabsorption modulators

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Face Mask Detection Using MobileNetV2 and VGG16 11th

Face Mask Detection Using MobileNetV2 and VGG16

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Kumar, Ujjwal Arora, Deepak Sharma, Puneet Department of Computer Science and Engineering Amity University Lucknow Campus Uttar Pradesh Lucknow India Dept. of CSE Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789811988240

As the COVID-19 situation is not over yet, a new strain of corona virus is again affecting population. Strain like Omicron and Deltacron still poses thread to the society. It is very necessary to keep our self-safe. To prevent spread of COVID few precautions are suggested by governments in the world like maintaining distance of 1 m, use of hand sanitizer, and always wear a mask. The new variant of COVID is now reported by the WHO on November 28, 2021;it was first designated as B.1.1.529 and then named as omicron and later a hybrid variant of delta and omicron was also reported. As these are affecting large population and seeing continuous straggle, it can conclude that corona virus can affect people for few more years considering the current scenario. Keeping that in mind people made face detection software which can be used to tell that a person wearing a mask not. This project is based on same object by using two different technologies MobileNetV2 and VGG16 so that a detail comparing can be done. By comparing both of them it can be known that which perform better and people can choose according to their necessity. This research paper is based on machine learning algorithm and deep learning using different Python libraries like OpenCV, TensorFlow with Keras, MobileNetV2, and VGG16. In this project, the main aim this to detect and then identify that person is wearing a mask or not then comparing both technologies and analyzes the result. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Viruses

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Classification and Prediction of Stroke Disease Using Machine Learning Algorithms

Classification and Prediction of Stroke Disease Using Machin...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Podutur Lahari Akarsh Kumar D. Usha Nandini M. D. Anto Praveena Mercy Paul Selvan Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Using text mining tools and machine learning algorithms, the paper presents a prototype for classifying strokes. The significance of machine learning extends across various domains, including surveillance, medicine, and data management, where appropriately trained algorithms prove invaluable. The study incorporates data mining techniques, offering a comprehensive understanding of information tracking from both semantic and syntactic perspectives. By analyzing medical case sheets, the proposed approach extracts symptoms and trains the system based on that information. A total of 507 case sheets are collected at "Sugam Multispecialty Hospital" in "Kumbakonam", Tamil Nadu, India, during the data collection phase. A tagging and maximum entropy approach was used to mine these case sheets. Stroke classification relies heavily on the suggested stemmer for distinguishing between common and special features. To handle the data that has been processed, support vector machines, XG Boost algorithms, SGD algorithms, decision trees, and random forests were used as machine learning algorithms. Random Forest demonstrated the highest accuracy among stroke classification algorithms evaluated. This integration of text mining and machine learning offers a promising framework for enhancing stroke classification based on patients' symptoms extracted from medical case sheets. The synergy between these technologies provides a robust methodology that can contribute significantly to improving the efficiency and accuracy of stroke diagnosis and classification in a medical.

关键词： Text mining Machine learning algorithms Accuracy Surveillance Stroke (medical condition) Tagging Syntactics

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Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Sumit Saklani Mahesh Manchanda Raman Sharma Devendra Singh Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Application Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments.

关键词： Deep learning Accuracy Rain Smart cities Scalability Urban planning Traffic control Real-time systems Safety Internet of Things

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Implementation of Dynamic Spectrum Access Techniques in Cognitive Radio Networks under Generalized Fading Conditions

Implementation of Dynamic Spectrum Access Techniques in Cogn...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Shobhit Garg Amulya Sharma Divyansh Devgan Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Management Studies Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

This paper is organized as follows: Section II provides a background on our research work based on DSA in CRNs using CR under different fading conditions. c) Performance: We also study how effective adaptive algorithms along with the machine learning can prove in achieving higher spectrum sensing accuracy and decision making on run time. By conducting simulations and extensive performance evaluation, we show that the traditional solutions fall short when compared to our AI-based methodology in terms of accuracy especially under harsh fading conditions. Finally, sensitivity analysis highlights the control parameters that need to be submitted to further optimization for better system performance. With all above analysis, our results indicate that DSA can alleviate spectrum underutilization problem and enhance communication reliability in CRNs. Thus, with proper adaptation strategies and machine learning algorithms, the DSA systems provide an appealing opportunity to meet the continuously increasing requirements of bandwidth-consuming applications in current days wireless networks. The results of this research are helpful in providing effective value input to Advances in the understanding and design of robust, efficient spectrum management approaches for dynamic and diverse wireless settings.

关键词： Fading channels Wireless sensor networks Accuracy Sensitivity analysis System performance Wireless networks Machine learning Sensors Reliability Optimization

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A Machine Learning Framework & Development for Insider Cyber-crime Threats Detection

A Machine Learning Framework & Development for Insider Cyber...

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2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023

作者： Yousef, Rasheed Jazzar, Mahmmoud Eleyan, Amna Bejaoui, Tarek Palestine Technical University - Kadoorie Tulkarem College of It Dept. of Computer Science Palestine Manchester Metropolitan University Department of Computing and Mathematics Manchester United Kingdom University of Carthage Computer Engineering Department Tunisia

ISBN: (纸本)9798350302523

Many organizations face a significant challenge with insider threats. As conventional security measures like intrusion detection systems and firewalls aren't always effective in detecting and preventing such threats. Insider threats often come from trusted individuals who possess knowledge of and access to important organizational assets. This work explores the use of machine learning to classify insider threat behaviors, specifically focusing on three approaches such that supervised, unsupervised, and reinforcement learning. The paper describes the development of an unsupervised machine learning system that analyzes data from multiple technical sources to detect malicious insider activity. The system, which is designed to be simple and easy to assemble, was tested with existing machine learning algorithms and showed moderate success in detecting malicious insider activity during the training phase and negligible success during the testing *** results suggest that while machine learning can be a useful tool for detecting insider threats, it should not be solely relied upon for threat detection. To improve the current system's performance, it is necessary to include additional features, such as file names, email subjects and headers, and website types. Furthermore, physical security, cybersecurity, psychological, and organizational factors must be considered when addressing insider threats. Future research should focus on acquiring real datasets, collecting insider threat scenarios and use cases, and testing different machine learning approaches from both technical and non-technical sources. © 2023 IEEE.

关键词： Cybersecurity

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