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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Kaur, Komalpreet Singh, Charanjit Kumar, Yogesh Punjabi University Dept. of Electronics & Communication Patiala India Pandit Deendayal Energy University Dept. of Computer Science & Engineering Gujarat Gandhinagar India

ISBN: (纸本)9798350398267

Congenital infections, disorders, or diseases occur when pregnant women get infected with an organism that further enters into their placenta and fetus after entering their bloodstream. Such conditions may affect newborn infants and unborn fetuses that need to be cured by early prediction. In the field of medicine, better high-performance prediction is achieved using Artificial intelligence. It is a broad area of science for simulating the natural intelligence established by animals and humans through machine learning. In this paper, we have given an overview of using AI algorithms to predict various congenital diseases and forward with a broad category of congenital disorders and infections that come under prenatal and neonatal categories. Later, a comparative table was formulated to study its effects on the embryo and fetus. In the case of performing tasks using ML or DL algorithms, specific steps have been followed that are given in detail under the framework section. In the last section of the paper, we have performed a comparative study on work done in predicting different congenital diseases using fuzzy logic, deep neural network, Ensemble learning, Support vector machine, Artificial neural network, Random forest, and Naïve Bayes. From the study, it has been found that although different ML or DL algorithms used in the individual prediction of different congenital diseases can give good outcomes, more work still needs to be done on proposing approaches for predicting hybrid methods. © 2022 IEEE.

关键词： Forecasting

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Deep Learning Enhanced Virtual Sensing: A Cost-Effective Approach for Water Quality Monitoring in Aquaculture

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood electronics 2025年

作者： Rasheed Abdul Haq, K.P. Harigovindan, V.P. Hsiung, Pao-Ann Electronics and Communication Engineering National Institute of Technology Puducherry Thiruvettakudy609609 India Department of Computer Science and Information Engineering National Chung Cheng University Chiayi62102 Taiwan

Efficient monitoring of water quality parameters is crucial for the successful management of aquaculture systems by ensuring the health and growth of aquatic life. Traditional sensor-based systems are often cost-prohibitive, limiting their adoption, particularly in resource-constrained settings. In this research, we introduce a cost-effective method leveraging virtual sensing to estimate dissolved oxygen (DO) levels using solubility and interdependency equations. Furthermore, we propose deep learning-enhanced virtual sensing (DLEVS) employing deep recurrent neural networks, including long short-term memory, gated recurrent unit (GRU), their bidirectional variants, and attention mechanisms, to accurately estimate DO. With extensive hyperparameter tuning, we have identified that the attention-based GRU (A-GRU) model is optimal, achieving significant reductions in RMSE from 0.3802 to 0.1534 for ADAK dataset and from 0.7505 to 0.0848 for MAC dataset in comparison with conventional virtual sensing methods. Our findings demonstrate that the DLEVS mechanism delivers precise DO estimation with substantially reduced costs, presenting an appealing solution for aquaculture water quality monitoring. © 2023 IEEE.

关键词： Long short-term memory

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Classification of Epileptic Seizures Disorder Using CWT and Deep Learning Algorithm from Electroencephalograms

Classification of Epileptic Seizures Disorder Using CWT and ...

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International Conference on Knowledge and Smart Technology (KST)

作者： S Alagesan R Murugan Suganya Devi K Ramanujam E Department of Computer Science and Engineering National Institute of Technology Silchar Silchar Assam India Department of Electronics and Communication Engineering National Institute of Technology Silchar Silchar Assam India

ISBN: (数字)9798331520403

ISBN: (纸本)9798331520410

An epileptic seizure is characterized by spontaneous, recurrent abnormal activity in the central nervous system, often presenting clinically as unusual motor, sensory, or psychomotor experiences. Epileptic seizure is a common chronic neurological disorder that can affect people of any age, race, social class, or location. This paper presents an approach that utilizes Continuous Wavelet Transform (CWT) to convert raw EEG signals into time-frequency images, which are then processed by the VGG-19 deep network model for feature extraction and the classification of epileptic seizures. Electroencephalograms (EEG) time-frequency images from the publicly accessible University of Bonn dataset were used to test the proposed model. The model performed exceptionally well with the state-of-the-art regarding accuracy, sensitivity, specificity, and F1-score. This approach represents a significant advancement in EEG-based epileptic seizure disorder classification, promising more accurate and reliable diagnostic tools.

关键词： Deep learning Neurological diseases Time-frequency analysis Continuous wavelet transforms Accuracy Sensitivity Brain modeling Motors Electroencephalography Reliability

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Analysis of Fake News Detection using Graph Neural Network (GNN) and Deep Learning

Analysis of Fake News Detection using Graph Neural Network (...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Prajwal Hiremath Srujan Sateesh Kalagi Mohana Electronics & Instrumentation Engineering RV College of Engineering Bengaluru India Electronics & Communication Engineering RV College of Engineering Bengaluru India Computer Science and Engineering (Cyber Security) RV College of Engineering Bengaluru India

The rise of fake news in social networks has become a significant concern, necessitating innovative strategies to combat misinformation. One such approach is the use of graph neural networks (GNNs) to trace and analyze false information dissemination. GNNs are specialized DL model capable of processing graph-structured data, making them ideal for capturing intricate dependencies and relationships in the spread of fake news. Proposed implementation starting with data preparation, where a carefully curated dataset is essential. The news stories are processed, tokenized, and transformed into a graph representation to better understand the relationships between words or entities. Graph construction enables the formulation of ML problems as graph classification tasks. Graph embedding, achieved through methods like word2vec or GNNs, plays a crucial role in representing each node as a low-dimensional vector, capturing its semantic significance within the network. This technique facilitates ML activities, such as node classification and link prediction. Combining graph embeddings and GNNs for classification allows for a more precise identification of true and false news, considering the structure and content of news propagation graphs. Finally, evaluating false news detection involves performance metrics such as accuracy. Proposed implementation obtained detection accuracy of 94%. Striking a balance between minimizing false positives and false negatives is crucial to combat misinformation effectively. By employing this approach, society can create a more trustworthy information environment, enabling individuals to make informed decisions and reducing the negative impact of fake news.

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A Perspective Analysis of Optical Biosensors in Machine Learning Applications

A Perspective Analysis of Optical Biosensors in Machine Lear...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： B. Naveen Kumar M. V. Panduranga Rao Preeta Sharan K. R. Varun Suresh Kallam M. Kumaresan Department of Computer Science and Engineering Faculty of Engineering and Technology Jain (Deemed-to-be-University) Bengaluru India Department of Electronics and Communication Engineering The Oxford College of Engineering Bengaluru India Department of Mechanical Engineering The Oxford college of Engineering Bengaluru India

Optical biosensors, a novel invention at the intersection of biotechnology and optics, are revolutionizing the field of biomedical diagnostics. Optical biosensors act by identifying variations in light’s wavelength, phase, polarization, or intensity caused by interactions between a sensing element and the target analyte. Optical biosensors outperform other sensor types in sensing applications due to their quick, accurate, and extremely sensitive results. When evaluating optical sensors, total power, core power, efficient area, and indices are the most crucial variables to consider. Recent years have seen notable advancements in machine learning (ML), a branch of artificial intelligence (AI). This review investigates the complementary nature of optical biosensors and machine learning methods. Here, the potential utility of ML for optical biosensors is explored in detail. The advantages and drawbacks of the most popular machine learning methods are listed based on an analysis of the optical biosensing data. It highlights the possible effects of machine learning on improving the sensitivity, selectivity, and adaptability of optical biosensing devices, and it summarizes current advancements in the field. Finally, the study considers the challenges and limitations that associated with current optical biosensing techniques.

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MHSA: A Multi-scale Hypergraph Network for Mild Cognitive Impairment Detection via Synchronous and Attentive Fusion

MHSA: A Multi-scale Hypergraph Network for Mild Cognitive Im...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yuan, Manman Jia, Weiming Luo, Xiong Ye, Jiazhen Zhu, Peican Li, Junlin Inner Mongolia University School of Computer Science Hohhot010000 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Northwestern Polytechnical University School of Artificial Intelligence Optics and Electronics Xi'an710072 China Inner Mongolia Autonomous Region People's Hospital Department of Imaging Medicine Hohhot010017 China

ISBN: (纸本)9798350386226

The precise detection of mild cognitive impairment (MCI) is of significant importance in preventing the deterioration of patients in a timely manner. Although hypergraphs have enhanced performance by learning and analyzing brain networks, they often only depend on vector distances between features at a single scale to infer interactions. In this paper, we deal with a more arduous challenge, hypergraph modelling with synchronization between brain regions, and design a novel framework, i.e., A Multi-scale Hypergraph Network for MCI Detection via Synchronous and Attentive Fusion (MHSA), to tackle this challenge. Specifically, our approach employs the Phase-Locking Value (PLV) to calculate the phase synchronization relationship in the spectrum domain of regions of interest (ROIs) and designs a multi-scale feature fusion mechanism to integrate dynamic connectivity features of functional magnetic resonance imaging (fMRI) from both the temporal and spectrum domains. To evaluate and op-timize the direct contribution of each ROI to phase synchronization in the temporal domain, we structure the PLV coefficients dynamically adjust strategy, and the dynamic hypergraph is modelled based on a comprehensive temporal-spectrum fusion matrix. Experiments on the real-world dataset indicate the effectiveness of our strategy. The code is available at https://***/Jia-Weiming/MHSA. © 2024 IEEE.

关键词： Magnetic resonance imaging

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IoT-Enabled Sleep Monitoring Wearables: Advancements in Tracking and Analysis

IoT-Enabled Sleep Monitoring Wearables: Advancements in Trac...

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computer, communication and Control (IC4), International Conference on

作者： K. Thinakaran V. Vijaya Baskar M. Ahsan Shariff Manduva Chervith G. Raghavendra S. Velmurugan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India M.Tech Computer Science and Engineering Sri Sai Ram Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electronics and Communication Engineering Mother Theresa Institute of Engineering and Technology Palamaner Andhra Pradesh India Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India

The importance of sleep to a person’s health and well-being cannot be overstated. Sleep monitoring and analysis control has been transformed by the combination of Internet of Things (IoT) technologies and wearable devices in recent years. This system analyzes the improvements achieved in sleep monitoring wearable enabled by IoT, providing a thorough and discreet method of evaluating sleep activities. Accelerometers and heart rate monitors are at the heart of these wearables, which also have complex processing units and connectivity modules. Insightful sleep quality analysis, precise sleep stage separation, and real-time data collecting are all made possible by this complementary relationship. Wearables may track sleep patterns, interruptions, and effectiveness by recording motion, heart rate variability, and brain activity through various sensors. Smart cloud interfaces that provide sleep metrics and trends improve the user experience and allow a deeper analysis of a person’s sleeping habits. In addition, these wearables may easily connect to smart home systems, enhancing sleep quality by modifying the house’s lighting and audio settings depending on the individual’s sleeping habits. The data gathered by these wearables has wider uses beyond personal sleep monitoring, such as helping the system better understand and treat sleep conditions.

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Deep Learning Methods for Video Segmentation and Tracking 15

Deep Learning Methods for Video Segmentation and Tracking

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Deshpande, Vivek Balaji, N.V. Sood, Gourav Ramya, G. Aggarwal, Prateek Debnath, Baishakhi Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Ug Mangement Karnataka Bangalore India

ISBN: (纸本)9798350370249

Deep mastering strategies for video segmentation and tracking involve using convolutional neural networks (CNNs) to pick out gadgets or areas of hobby in the body of a video. Such fashions may be used to extract spatial statistics, which include an item's role, movement, and velocity from the video. Moreover, they are able to help locate changes in the environment, hit upon appearances of recent items, and tune multiple objects within the identical body. It is an effective device for video forensics and movement evaluation, permitting computerized tracking and identity of objects in a scene. Maximum contemporary procedures observe CNNs for single-frame item segmentation and monitoring, which is predicated on the excessive-stage shape and look facts of interested items. Furthermore, CNNs may be used for temporal analysis and fusion of the collection of frames for extra robust object detection and tracking. Such an approach is regularly employed for multi-item monitoring and hard item segmentation duties. In the end, using CNNs in video segmentation and monitoring can result in advanced accuracy and robustness of video information. © 2024 IEEE.

关键词： Changes Contemporary Detection Monitoring Movement Permitting Robustness Segmentation

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A Hexagonal Fractal Antenna with Switchable Radiation Pattern for Network Enabled On-Body Wireless communications 3

A Hexagonal Fractal Antenna with Switchable Radiation Patter...

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3rd IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2023

作者： Bhanu Satpathy, Rudra Ramesh, G.P. Alzubaidi, Laith H. Chanti, Yerrolla Hussein, A.H.A. Sai Theja, Badrireddy Chennai Department of Electronics and Communication Engineering India The Islamic University Najaf Iraq School of Computer Science & Artificial Intelligence SR University Warangal Telangana India College of Pharmacy Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

This research presents a novel approach to enhance network communication through the development of a Hexagonal Fractal Antenna (HFA). The exponential growth in wireless communication demands innovative antenna solutions that can cater to evolving connectivity needs. This study explores the application of fractal geometry, specifically hexagonal patterns, to antenna design, offering a compact, multiband, and wideband solution that addresses the challenges of modern network communication exhibits superior performance characteristics, making it ideal for diverse wireless applications. Its compact form factor enables deployment in both urban and rural settings, where space constraints are prevalent. These results were compared with existing methods namely such as Omnidirectional Dual Polarized Low- Profile Textile Antenna (ODLPTA), RF- Harvesting Tightly Coupled Antenna (RF-TCRA) and Dual Polarized Embroidered Textile Armband Antenna (DP-ETTA). When compared to existing methods the suggested method achieved better performance in terms of Network efficiency of 98.4 %, Gain of 1.8% and Peak of 92.3 respectively. © 2023 IEEE.

关键词： Antenna Gain Hexagonal Fractal Antenna Network Efficiency Radiation Pattern and Wireless communications

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Navigating the Dark: Advances in Robotic Night Patrol with D-Block Mask Electric EEL Dense Nested R-CNN for Enhanced Safety 4

Navigating the Dark: Advances in Robotic Night Patrol with D...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Kumar, V. Vinay Shrimali, Manish Shaik, Nazeer Ranjitha, K.N. Garg, Neha Maranan, Ramya Anurag University Department of Electronics and Communication Telangana Hyderabad500088 India Department of Computer Science and It Rajasthan Udaipur313001 India Andhra Pradesh Bapatla522102 India Bgs Institute of Technology Department of Physics Karnataka BG nagara571448 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Ensuring safety and security during nighttime operations remains a critical challenge for automated surveillance systems, often hindered by limited visibility, sensor noise, and inadequate data processing techniques. To overcome these limitations, an advanced robotic night patrol system is proposed, utilizing the D-block Mask Electric Eel Dense Nested R-CNN (D-Mask-EEDN-R-CNN). This system acquires data from multiple sensors implemented on robotic units, which saves it in a Firebase database instantly. The noise that hinders the correct interpretation of the data is eliminated and data quality improved in the pre-processing stage with the help of Improved Sage-Husa Adaptive Kalman Filter (SHAKF). The data is then passed through feature extraction and, classification by the proposed D-Mask-EEDN-R-CNN network which is fine-tuned by the E2FOP for better performance. This system has been developed in Python and the efficiency of this system, in fact, is very high, it is 99.9% accuracy and 99.8% improvements on average thus yielding better recall results than existing methods. These results highlight the effectiveness of the proposed approach in enhancing the accuracy and reliability of robotic night patrols, providing a robust solution for improving nighttime safety and security. © 2024 IEEE.

关键词： Adaptive filters

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