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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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Urban Vertical Farming with IoT and Precision Monitoring for a Sustainable Future

Urban Vertical Farming with IoT and Precision Monitoring for...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Unni Krishnn, A. Dutt, Amit Singh, Jay Bhavana, Munugapati Baswaraju, Swathi Mohammed, Baydaa Fathil Venkatraman, Akila Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Lovely Professional University Phagwara India Gl Bajaj Institute of Technology and Management Dept. of Eee U.P. Gr. Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Computer Science-Data Science Bangalore India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Griet Department of Information Technology Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

The fast expansion of population worldwide and urbanisation had been estimated to have 80 % of the population in urban areas in the upcoming 50 years leads to an exponential rise in demand of food. These needs provide an urgent call for developing novel approaches to achieve global food demands amid sustainability problems. Urban Vertical Farming (UVF) offers an attractive solution for growing crops in the vertical dimension of structures, the urban skyscrapers, where land use is limited. However, it is fairly limited research which applies the current state of art in cutting edge technologies such as the Internet of Things, to try to automate and monitor farm processing. Thus, in this review paper, gaps in existing research, the potential for IoT enabled vertical farming, as well as challenges such as energy use, economic feasibility and urban policy integration are examined. Vertical farming can be enhanced through resource efficiency, scalability and level of sustainability through the use of IoT systems and AI driven data analytics which will also help promote urban food security. The research gives a summary of the current state, technological capabilities, and potential way forward in the process of successful vertical farming implementation in urban sets. © 2025 IEEE.

关键词： Food security

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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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Ultra-High Frequency Spinal Nerve Neuromodulation for Improving Bladder Continence: Implications for Overactive Bladder Management

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Neuromodulation 2025年第5期28卷 796-802页

作者： Lin, Bor-Shing Khasanah, Nurida Cai, Chun-Ying Wu, Chun-Wei Lu, Kuo-Hsiang Lin, Wei-Tso Peng, Chih-Wei Department of Computer Science and Information Engineering National Taipei University New Taipei City Taiwan Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei Taiwan Department of Obstetrics and Gynecology Faculty of Medicine Public Health and Nursing Universitas Gadjah Mada–Dr Sardjito General Hospital Yogyakarta Indonesia School of Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei Taiwan Gimer Medical Co. Ltd New Taipei City Taiwan School of Gerontology and Long-Term Care College of Nursing Taipei Medical University Taipei Taiwan

Objective: Ultrahigh frequencies (UHF) have been shown to selectively suppress the sensory pathway with a rapid onset and prolonged effect compared with low frequencies. Few studies have explored the feasibility of UHF electrical stimulation in treating overactive bladder. This study aimed to investigate whether bladder overactivity could be inhibited by UHF stimulation at the L6 nerve root. Materials and Methods: Female Sprague-Dawley rats (n = 12) were divided into two groups: sham and UHF groups. Bladder overactivity was induced by continuous intravesical infusion of 0.5% acetic acid (AA). UHF L6 nerve root stimulation (500 kHz, 20 mA for 5 minutes) was applied to the rats in the UHF group. To investigate the effects of the treatment, intravesical pressure was recorded by cystometrography during continuous transvesical infusion, with volume threshold (VT) and intercontraction interval (ICI) used to conduct the investigation. Results: Bladder overactivity was successfully developed in all rats with a significant decrease of median VT and ICI to 83.7% and 86.4%, respectively. UHF stimulation of the L6 nerve root was able to counteract the AA effect by significantly increasing median VT and ICI to 220% and 36.1%, respectively;these effects persisted for ≥two hours. There was a significant difference in the effects of UHF electrical stimulation between the sham and UHF groups (p < 0.05). Conclusion: This preliminary study provides evidence for UHF stimulation of the L6 spinal nerve root, analogous to the sacral nerve root in humans, as a potential alternative neuromodulation technique to suppress bladder overactivity. © 2024 The Authors

关键词： Electrical stimulation neuromodulation overactive bladder spinal nerve ultrahigh frequency stimulation

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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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ChatGPT Code Detection: Techniques for Uncovering the Source of Code

arXiv

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arXiv 2024年

作者： Oedingen, Marc Engelhardt, Raphael C. Denz, Robin Hammer, Maximilian Konen, Wolfgang Cologne Institute of Computer Science Faculty of Computer Science and Engineering Science TH Köln Gummersbach Germany Department of Medical Informatics Biometry and Epidemiology Faculty of Medicine Ruhr-University Bochum Bochum Germany

In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by artificial intelligence (AI). As these technologies evolve rapidly, it is crucial to explore how they influence code generation, especially given the risk of misuse in areas like higher education. This paper explores this issue by using advanced classification techniques to differentiate between code written by humans and that generated by ChatGPT, a type of LLM. We employ a new approach that combines powerful embedding features (black-box) with supervised learning algorithms – including Deep Neural Networks, Random Forests, and Extreme Gradient Boosting – to achieve this differentiation with an impressive accuracy of 98%. For the successful combinations, we also examine their model calibration, showing that some of the models are extremely well calibrated. Additionally, we present white-box features and an interpretable Bayes classifier to elucidate critical differences between the code sources, enhancing the explainability and transparency of our approach. Both approaches work well but provide at most 85 − 88% accuracy. We also show that untrained humans solve the same task not better than random guessing. This study is crucial in understanding and mitigating the potential risks associated with using AI in code generation, particularly in the context of higher education, software development, and competitive programming. © 2024, CC BY.

关键词： Deep neural networks

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A review of Communication and Data Networks Algorithms Shaping Modem Connectivity 7

A review of Communication and Data Networks Algorithms Shapi...

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7th International Conference on Contemporary Computing and informatics, IC3I 2024

作者： Srinath, M.K. Gatla, Anitha Lakhanpal, Sorabh Sharma, Saurabh Reddy, B.Ravali Al-Rubaye, Taqi Mohammed Khattab New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Institute of Aeronautical Engineering Department of Information Technology Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201314 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India The Islamic University College of Medical Technologies Department of Medical Laboratory Technology Najaf Iraq

ISBN: (纸本)9798350350067

The world of today is highly interconnected and undergoes rapid changes. Algorithms are essential for data networks and communication systems. This study looks at how the complexity of algorithms affects network design. It focusses on the GROA (Global Routing Optimisation Algorithm), LBEA (Load Balancing and Efficient Allocation), and QKD-NS systems in particular. We aim to educate individuals on the impact of various methods on security, route completion time, and resource constraints. Based on the results, GROA creates variable paths, QKD-NS boosts safety with quantum-based innovations, and LBEA efficiently divides up resources based on what the network requires. According to the study, putting these many technologies together could lead to strong partnerships that can handle security breaches, traffic jams, and changes in the network. This study focusses on how advances in technology have led to better computer design, which in turn has made the internet safer and easier to use. The paper elucidates the application of these novel mathematical discoveries to enhance the performance and durability of data networks. These new details help communication networks improve by ensuring that they are safe and can meet today's link standards. © 2024 IEEE.

关键词： Traffic congestion

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