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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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Artificial Intelligence based Models to Support Water Quality Prediction using Machine Learning Approach

Artificial Intelligence based Models to Support Water Qualit...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： William, P. Oyebode, Oluwadare Joshua Ramu, Gandikota Gupta, Manish Bordoloi, Dibyhash Shrivastava, Anurag Sanjivani College of Engineering Department of Information Technology Savitribai Phule Pune University Pune India Afe Babalola University Civil and Environmental Engineering Ekiti State Ado-Ekiti Nigeria Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad500043 India Lovely Professional University Division of Research and Development Phagwara India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India

ISBN: (纸本)9798350333244

The purpose of this study is to simulate the propagation of bio-contamination risk in Water Distribution System (WDS). This will be accomplished by putting an emphasis on source identification and response modelling. This will be accomplished in the WDS via the process of simulating the bio-contamination risk propagation under real environmental circumstances. This will be done in order to accomplish this goal. Due to the failure to take into consideration the ability for the bulk of the pollutants to undergo chemical degradation and the neglecting of dynamic responses, an overestimation of the population that is exposed has resulted. The study presented here takes into account the connected change in a variety of water quality indicators that are caused by the mixing of the pollutants. These measurements include the total organic carbon content, pH, and alkalinity. In practise, the baseline requirements for each of the aforementioned metrics are set at the beginning of the regular WDS monitoring. Contamination is then indicated by considerable deviations from the base line. The study's goal was to determine whether it is feasible to develop a smart monitoring system powered by artificial intelligence (AI) that would successfully enable water operators to ensure near real-time quality control for the early detection of chemical and/or biological pollution and risk management. The study's goal was to see whether it was possible to develop an artificial intelligence (AI)-based smart monitoring system. The study's purpose was to investigate the viability of building a sophisticated monitoring system that would allow water operators to perform quality control in near real time. Artificial Neural Networks (ANN) and other sophisticated pattern recognizers like Support Vector Machines (SVMs) are examples of cutting-edge sensor technologies that have been implemented as solutions to identify anomalies and estimate the severity of such irregularities. © 2023 IEEE.

关键词： Support vector machines

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Analysis of the Performance of Software Defined Networks (SDN) Versus Networks with TCP/IP Architecture 2

Analysis of the Performance of Software Defined Networks (SD...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Tanguturi, Rama Chaithanya Amutharaj, J. Prakash, N. Subramani, K. Taqui, Syed Noeman Sree Southry, S. Pace Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole India Raja Rajeswari College of Engineering Department of Information Science and Engineering Bengaluru560074 India Kumaraguru College of Technology Department of Electrical and Electronics Engineering Coimbatore India Department of Physics Tamil Nadu Dindigul India Saveetha School of Engineering Department of VLSI Microelectronics Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India Sona College of Technology Department of Electronics and Communication Engineering Tamilnadu Salem India

ISBN: (纸本)9798350327533

It is expected that the transfer speeds on average would rise from 39Mbps to 75.4Mbps. For this reason, the idea of implementing centralized networks, with easy administration and traffic management through policies, has become essential in recent years. Software-defined networks (SDN) and traditional TCPIIP architecture networks are two distinct approaches to network design and management. While TCPIIP architecture networks have been the norm for decades, SDN is a relatively new concept that promises to revolutionize network performance, flexibility, and scalability. In this work, the performance of SDN versus networks with TCPIIP architecture is analyzed and compared. In the analysis of the performance of software-defined networks and networks with TCPIIP architecture, it is predicted to have a favourable response in the evaluation of different metrics such as latency, jitter and bandwidth in both networks;but with better performance in the case of SDN networks. © 2024 IEEE.

关键词： Flexibility and Adaptability Integration with Cloud Services Management and Configuration Security Software-defined networks (SDN)

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A Novel Development of Artificial Intelligence Enabled Learning Methodology for Human Depression Prediction Scheme

A Novel Development of Artificial Intelligence Enabled Learn...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Ramkumar, G. Priyadharshini, R. Selvi, K. Kalaiselvi, K. Rathika, S. Kanagavalli, N. Saveetha University Department of Ece Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.K Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

Human Depression Prediction is essential for several reasons, primarily centered around improving mental health outcomes and providing timely interventions. Firstly, early detection of depression allows for prompt and targeted interventions, enabling individuals to receive appropriate support and treatment before their condition worsens. The Human Depression Prediction Scheme (HDPS) introduces an innovative method for predicting depression by synergizing the capabilities of deep learning through the LeNet architecture with the optimization prowess of the Grey Wolf Optimization (GWO) algorithm. The proposed scheme achieves a commendable accuracy of 95%, attesting to its effectiveness in discerning depressive tendencies. Notably, the HDPS is implemented in Google Colab, emphasizing its accessibility and ease of use. This integration of advanced technologies and optimization techniques positions HDPS as a promising tool for early detection of depression, offering both high accuracy and practical implementation in a widely accessible computing environment. © 2024 IEEE.

关键词： Forecasting

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Experimental Analysis of Human Behavior Monitoring System using Gaze Shift Principles and Bayesian Image Foraging Logic 3

Experimental Analysis of Human Behavior Monitoring System us...

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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Vijayakarthik, P. Nithiyanandam, N. Dhanasekaran, S. Kumar, A. Sivanesh Gobinath, D. Maheswari, S. Sir M Visvesvaraya Institute of Technology Department of Information Science and Engineering Bengaluru India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Kalasalingam University Department of Information Technology Tamilnadu Srivilliputtur India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Sri ManakulaVinayagarEngineering College Department of Information Technology Puducherry India St.Joseph's College of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

To gather the most relevant data as well as information for a given activity, this paper looks at the challenge of focusing our attention on subsets of video streams. This is a foraging problem, which is how the challenge of monitoring is classified. Observer attentiveness may be modeled as foraging behavior using the developed probabilistic framework. The forager changes its focus from one stream or camera to the next based on what it sees, whether it's fascinating items or activity. Multi-stream video summary can benefit from the technique shown here. Furthermore, it may be used in conjunction with advanced video surveillance, such as activity and behavior analysis, as a first stage in the process. As a demonstration of the suggested technique's utility, findings from an experiment using a publicly available data set are offered. © 2022 IEEE.

关键词： Behavioral research

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Machine Learning Techniques for Protein Structure Prediction in Bioinformatics

Machine Learning Techniques for Protein Structure Prediction...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Abdiqayumovich, Aliyev Nurillo Jaisudha, J. Al-Farouni, Mohammed Khare, Neha Kaur, Gurmandeep Koduru, Siva Sai Fergana Medical Institute Of Public Health Assistants Of The Department Of Biophysics And Information Technologies Fergana Uzbekistan Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Saveetha Institute of Medical and Technical Sciences Dapartment of Ai & Ml Tamilnadu Chennai India

ISBN: (纸本)9798350390759

This work analyses and discusses the use of machine learning methods in the field of bioinformatics concerning protein structure prediction. The steps of the proposed methodology include: acquiring and cleaning the relevant proteins sequences datasets;and creation and training of deep learning models, including Convolutional Neural Networks (CNNs), and Recurrent Neural Networks, (RNNs). Overall, adding data augmentation and transfer learning boosts the performance of the models to a mean accuracy of 87%. Predictors Evaluation: In evaluating the predictors, the model used an accuracy of 6% and a Matthews correlation coefficient (MCC) of 0. 74. The generality and efficiency of the proposed model are tested and compared with results from other similar standard datasets. Study shows that the results of the model have higher reliability than the traditional method of the machine-learning algorithm especially in the classification of the second and third levels of varied types of proteins. The research will demonstrate the applicability and possibility of the large-scale and cost-efficient approaches to the protein structure prediction using machine learning, while emphasizing the drawbacks of experimental tools. Recommendations for future work are to enhance the interpretability of the resulting models, better combine methods based on the use of information from hybrid schemes with physical models, and use more extensive databases for enhancing accuracy. This research highlights the profound impact that machine learning can have in the field of bioinformatics as a tool to enhance knowledge of such protein structures and to further practice improvements in the understanding of life processes and a and drug design. © 2024 IEEE.

关键词： Federated learning

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Design and Development of an IoT based Intelligent Energy Meter using Less Expensive Controller Unit

Design and Development of an IoT based Intelligent Energy Me...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Balasubramani, M. Gohin, B. Jayashankari, J. Samuthira Pandi, V. Ravi, Kiran Chand Arumugam, Deepak V.S.B Engineering College Department of Computer Science and Engineering Tamil Nadu Karur India Swarnandhra College of Engineering and Technolnogy Department of Computer Applications Andhra Pradesh Seetharamapuram India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Tamil Nadu Chennai India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electrical and Electronics Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

With an eye towards improving the efficacy of energy monitoring and management, this research offers a thorough approach for the creation of an Intelligent Energy Metre that is based on the Internet of Things. A key component of the system is the use of specialized sensors to continuously measure electrical characteristics like voltage and current. These sensors generate analog signals, which are conditioned, digitized, and processed by a microcontroller. Algorithms within the firmware/software calculate energy consumption metrics, which are transmitted wirelessly to a central server or cloud platform using secure communication protocols. The collected data is then stored and analyzed to provide insights into energy usage patterns, facilitating informed decision-making for optimizing energy efficiency and reducing costs. The methodology encompasses various stages, including market research, hardware and software development, integration, testing, deployment, and maintenance. Through the implementation of this methodology, a robust and scalable IoT-based energy monitoring system can be developed, empowering users to make informed decisions for sustainable energy management. © 2024 IEEE.

关键词： Energy management

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CTGGAN:A modified generative adversarial network for imbalanced CTG signal classification during labor

CTGGAN:A modified generative adversarial network for imbalan...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Mai, Peilin Feng, Junyuan Li, Luoyi Chen, Qinqun Liu, Guiqing Wei, Hang Guangzhou University of Chinese Medicine School of Medical Information Engineering Guangzhou China Shenzhen University College of Computer Science and Software Engineering Guangzhou China Guangzhou University of Chinese Medicine The First Affiliated Hospital Guangzhou China Guangzhou University of Chinese Medicine Intelligent Chinese Medicine Research Institute Guangzhou China

ISBN: (纸本)9798350386226

Cardiotocography (CTG) is a primary tool for real-time monitoring fetal heart rate (FHR) and uterine contraction (UC) signals during labor. Nowadays CTG signal classification models based on deep learning have facilitated end-to-end CTG interpretation through deep neural networks for automatic feature extraction. However, the extreme rarity of abnormal cases in clinical settings has posed great challenges for CTG deep learning. Meanwhile, most Generative Adversarial Network (GAN)-based studies focused on FHR signal generation and ignored the importance of UC signals during labor. Hence, we introduced CTGGAN, a modified GAN specifically designed to simulate both FHR and UC intrapartum signals simultaneously. Our approach incorporated Wasserstein Distance to prevent gradient vanishing during training, alongside a set of latent code vectors that enhance the generator controllability. We also integrated an auxiliary classifier to impose categorical constraints, thereby enriching the diversity of the generated samples. The experimental results demonstrated that CTGGAN effectively generated CTG signals that closely resembled real CTG signals. In addition, the incorporation of CTGGAN significantly improved performance, with a 19.18% increase in sensitivity over the traditional classification without GAN. Furthermore, the comparison with several state-of-the-art CTG classification models underscored the discriminative performance of our CTGGAN. In conclusion, this study effectively enhanced the detection of abnormal cases for intrapartum fetal monitoring. © 2024 IEEE.

关键词： Neonatal monitoring

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Analyzing the behaviors of pedestrians and cyclists in interactions with autonomous systems using controlled experiments: A literature review

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Transportation Research Part F: Traffic Psychology and Behaviour 2025年 114卷 270-307页

作者： Danya Li Wencan Mao Francisco C. Pereira Yu Xiao Xiang Su Rico Krueger Department of Technology Management and Economics Technical University of Denmark (DTU) Denmark Information Systems Architecture Science Research Division National Institute of Informatics (NII) Japan Department of Information and Communications Engineering Aalto University Finland Department of Computer Science and Department of Agricultural Sciences University of Helsinki Finland

Urban transportation is set to undergo a profound transformation with the advent of autonomous systems such as autonomous vehicles, automated buses, and sidewalk delivery robots. To promote safe, sustainable, and inclusive urban mobility, understanding and predicting the behaviors of pedestrians and cyclists, including their intentions, decisions, and movements, when they interact with autonomous systems becomes crucial. Gaining a thorough understanding of these complex interactions can not only improve the safety, efficiency, and acceptance of autonomous systems but also enhance the design and implementation of these technologies. Through a comprehensive review of the literature spanning the years 2014 to 2023, we identify 99 articles that empirically investigate the interactions of humans and autonomous systems. Based on our overview of progress and challenges within this field, we further identify five research gaps that future research should address to enhance human-autonomous system interactions, including: (1) scaling up experimental scenarios to multi-user and multi-modal setups to better represent real-world challenges, (2) emphasizing safety-critical scenarios that are difficult to achieve in real-world environments by applying virtual reality, (3) incorporating diverse behavioral data from the human perspective to deepen the understanding of vulnerable road user behavior and decisions, (4) embracing continuous and real-time interaction to better predict dynamic future environments, and (5) enhancing the generalization ability to ensure realism and broad applicability. This review article offers valuable insights for the growing human-autonomous system research community, specifically those interested in leveraging controlled experiments to enhance the understanding and prediction of pedestrians' and cyclists' behaviors in future urban environments.

关键词： Human-autonomous system interaction Human behavioral modeling Autonomous vehicles Virtual reality Controlled experiment

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Application of Machine Learning to identify factors affecting prevalence of Brucella: A Central India Study

Application of Machine Learning to identify factors affectin...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Dubey, Madhuri Makhijani, Richa Parate, Mayur Nayak, Amit Singh, Lokendra Kashyap, Rajpal Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Maharashtra Nagpur India Advance Research Centre Central India Institute of Medical Sciences Maharashtra Nagpur India

India is an agricultural country, and a large portion of the population is regularly exposed to animal rearing, which increases the risk of zoonotic diseases. The Covid-19 pandemic is a recent and deadly example of zoonotic disease transmission, according to the World Health Organization. In the past three decades, multiple infectious diseases have been transmitted between animals and humans. One such bacterial infection is Brucellosis, caused by factors such as exposure to animals, consumption of raw milk, dairy products, and meat, along with certain lifestyle habits. In this study, a cross-sectional surveillance-based investigation was conducted to assess the prevalence of zoonotic infections in the Central Indian population. The study focused on three states: Maharashtra, Madhya Pradesh, and Chhattisgarh, where 7026 samples were recruited under strict inclusion and exclusion criteria. Data on participants' lifestyle, dietary habits, and animal exposure were collected through structured questionnaires, followed by serum testing for Brucella antibodies using the Novatec IgM detection kit, with a sensitivity and specificity of over 95%. To analyse the dataset, four machine learning classifiers were employed: Random Forest, Logistic Regression, Support Vector Classifier (SVC), and Decision Tree. The classifiers were applied to the data both before and after handling data imbalance and encoding categorical variables. The performance of the models was evaluated using accuracy, precision, recall, and F1-score as metrics. Among the models, Random Forest achieved the highest accuracy of 99.11%, followed by SVC with 96.47% accuracy. The feature importance analysis revealed that animal exposure and dietary habits were the most significant predictors of Brucellosis risk. This study demonstrates the potential of machine learning techniques in predicting zoonotic infections such as Brucellosis by integrating lifestyle and clinical data. The results indicate that combining epid

关键词： Feature Selection

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