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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Bhat, Pushkar Manohara Pai, M.M. Pai, Radhika M Manipal Institute of Technology Department of Information and Communication Technology Manipal India Manipal Institute of Technology Department of Data Science and Computer Applications Manipal India

ISBN: (纸本)9798350338607

Anomaly detection is a significant challenge that exists in multiple industries, including healthcare, finance, and manufacturing. Machine Learning techniques are extensively being used to solve these problems. However, data privacy concerns and regulations are making acquiring the necessary data for model training increasingly challenging, particularly in sensitive sectors. Federated learning has emerged as a promising solution, allowing local models to be trained on edge devices using local datasets and model parameters to be shared with a central server for aggregation. This allows the models to reap the benefits of the distributed data without actually sharing any data. However, the current aggregation method used in federated learning is susceptible to models trained on bad data, which can impact the accuracy and reliability of the resulting model. To address this issue, the study proposes a new filtered aggregation algorithm that greatly mitigates the effect of models trained on bad data. The proposed algorithm accounts for models that cannot detect anomalous patterns and gives them much less representation in the final model, resulting in improved accuracy and reliability. © 2023 IEEE.

关键词： Anomaly detection

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Air quality and dust level monitoring systems in hospitals using IoT

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Discover Internet of Things 2025年第1期5卷 1-18页

作者： Parkavi, A. Sowmya, B.J. Alex, Sini Anna Supreeth, S. Shruthi, G. Rohith, S. Chatterjee, Sudipta Lingaraj, K. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India Department of Electronics and Communication Engineering Brainware University Barasat West Bengal Kolkata700125 India Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Bellary583104 India

This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that focus on fewer parameters. The use of IoT technology enables real-time monitoring and alerts, allowing hospital staff to respond immediately to any issues. Additionally, predictive analytics anticipate future changes, giving staff time to take preventive measures. The integration with a mobile app and cloud-based data storage ensures easy access to data, convenient monitoring, and the ability to scale the system across multiple hospital facilities, catering specifically to the unique needs of different healthcare environments. © The Author(s) 2025.

关键词： Indoor air pollution

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Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

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Soft Computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

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A Hand Gesture-Operated System for Rehabilitation Using an End-to-End Detection Framework

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 698-708页

作者： Dutta, H Pallab Jyoti Bhuyan, M.K. Neog, Debanga Raj Macdorman, Karl F. Laskar, R.H. Department of Electronics and Electrical Engineering Indian Institute of Technology Assam Guwahati781039 India Department of Electronics and Electrical Engineering and Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Assam Guwahati781039 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute ofTechnology Assam Guwahati781039 India School of Informatics and Computing Indiana University IN46202 United States Department of Electronics and Communication Engineering National Institute of Technology Assam Silchar788010 India

In this article, we propose a hand gesture-operated system as an AI application to relieve discomfort and restore function in hand and arm movements caused by injuries and nerve and muscle complications. The system trains patients with hand exercises, such as performing hand gestures accurately, traversing within specified bounds, and operating a hand gesture calculator. However, the system requires accurate hand gesture detection, which is impeded by background clutter and variations in illumination and in the hand's size and angle. To address this, we developed a robust hand detection module that uses a single-stage transformer deep network. The transformer network encodes global information and uses bipartite matching to reduce the frequency of spurious detections. It drives a regression head and a classification head to localize the hand gesture in a bounding box and assign it a class label. Hand keypoints are also detected to support drawing, path traversal, and calculator use. The approach is evaluated on two benchmark datasets: 1) OUHANDS and 2) NUS. The method yields 89.6% accuracy for OUHANDS and 100% for NUS. These results indicate that precise hand detection can support a robust system for rehabilitation through hand exercises. Our experiments confirm that the users' hand function progressively improved. © 2023 IEEE.

关键词： Deep neural networks

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Performance analysis of various classification algorithms for providing competency training to workplace risk prevention

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Multimedia Tools and Applications 2025年第15期84卷 15123-15149页

作者： Garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Birla Institute of Technology Ranchi Mesra India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India

In the workplace, risk prevention helps detect the risks and prevent accidents. To achieve this, workers' mental and physical parameters related to their health should be focused on and analyzed. It helps improve the workers' mental health and thus improves organizational performance. There are a lot of mental illnesses due to excess work and a lack of professional knowledge. It affects the performance of the worker. So, the organization needs to conduct competency training programs to improve their professional skills and the mindset to tackle the work pressure. There are several competency training programs given in various organizations to mold the workers, and it considerably reduces workplaces risks. Competency training in the workplace teaches integrity, honesty, managerial skills, and other workers. Developing organizational skills is the primary concern in this competency training. This paper enabled one of the best machine learning algorithms to be chosen to predict the hazards in the workplace and provide a healthy environment to the workers. For that, several classification methods such as Logistic Regression, Decision trees, AdaBoost classifier, Random Forest, Gradient Boosting classifier, and XGBoost classifier are implemented experimented with, and the performance is verified with python software. From the comparison, it has been found that the XGBoost classifier is selected as the best classifier and identified that it is highly suitable for analyzing and predicting workplace risks data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Occupational risks

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Robust twin-field quantum key distribution through sending or not sending

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National science Review 2023年第4期10卷 76-85页

作者： Cong Jiang Zong-Wen Yu Xiao-Long Hu Xiang-Bin Wang Jinan Institute of Quantum Technology State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Data Communication Science and Technology Research Institute School of Physics State Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-sen University Shanghai Branch CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Shenzhen Institute for Quantum Science and Engineering and Physics Department Southern University of Science and Technology Frontier Science Center for Quantum Information

The sending-or-not-sending(SNS) protocol is one of the most major variants of the twin-field(TF)quantum key distribution(QKD) protocol and has been realized in a 511-km field fiber, the farthest field experiment to date. In practice, however, all decoy-state methods have unavoidable source errors, and the source errors may be non-random, which compromises the security condition of the existing TF-QKD protocols. In this study, we present a general approach for efficiently calculating the SNS protocol's secure key rate with source errors, by establishing the equivalent protocols through virtual atenuation and the tagged model. This makes the first result for TF QKD in practice where source intensity cannot be controlled exactly. Our method can be combined with the two-way classical communication method such as active odd-parity pairing to further improve the key rate. The numerical results show that if the intensity error is within a few percent, the key rate and secure distance only decrease marginally. The key rate of the recent SNS experiment in the 511-km field fiber is still positive using our method presented here, even if there is a ±9.5% intensity fluctuation. This shows that the SNS protocol is robust against source errors.

关键词： quantum information quantum key distribution sending or not sending twin field source error

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Brain Tumor Localization and Multimodal Segmentation in MRI Images by Automated Process

Brain Tumor Localization and Multimodal Segmentation in MRI ...

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2024 International Conference on Knowledge Engineering and communication Systems, ICKECS 2024

作者： Kaviya, S. Ricky, A.Sam Gowtham, S. Nath, Srigitha.S. Bharathi, R.Jennie Praveena, N.G. Saveetha Engineering College Department of Electronics and Communication Engineering Chennai India Nmv Institute of Science and Technology Department of Artificial Intelligence and Data Science Chennai India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350359688

In neuro-oncology, precise MRI tumor segmentation and localization are essential for efficient diagnosis and therapy planning. Although automated segmentation methods have advanced, there are still difficulties in reaching high efficiency and accuracy. A new ML framework for automated multimodal brain tumor localization and segmentation in MRI imaging is proposed. The model incorporates various MRI modalities to improve segmentation accuracy and localization precision by using CNNs and DL architectures. The quality of MRI datasets is standardized and enhanced by data preprocessing approaches, and model training optimizes validation measures and loss functions to assess performance. The suggested model yields significant gains in tumor segmentation accuracy and localization precision compared to earlier methods. Evaluation measures show that the model performs better than earlier approaches on various MRI datasets, including Sensitivity, Specificity, and Dice Similarity Coefficient. The results highlight the effectiveness of the suggested machine learning framework in automating the segmentation and localization of brain tumors, providing a dependable tool to support physicians in the practice of neuro-oncology. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Illumination Effects on Deep Fake Detection: Performance Comparison of CNN Architectures

Illumination Effects on Deep Fake Detection: Performance Com...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Yangoti, Sai Bhavya Dumpala, Rohith Punyamurthy, Vijay Jatoth, Ravi Kumar Indian Institute of Information Technology Design and Manufacturing Dept. of Artificial Intelligence and Data Science Kurnool India National Institute of Technology Dept. of Electronics and Communication Engineering Warangal India

ISBN: (纸本)9798350379716

While AI has many advantages, it also has a few drawbacks associated with 'deep fakes.' Deep fakes involve the creation of very realistic videos and images realized through technologies that work with facial and voice modalities for deceptive purposes. Researchers have developed various techniques to detect these manipulated videos and images, employing complex neural network models such as convolutional neural networks (CNNs). However, deep fakes are very difficult to identify due to lighting and shadow effects that may hide some signs of manipulation. No one technique of detection could have the ability of identifying the tampered images under all kinds of illumination conditions. This paper compares different CNN models-ResNet50, EfficientNet, and MobileNetV2-to improve detection accuracy so as to be more resistant to new deep fake techniques, in particular under different illumination conditions. The importance of the study lies in arriving at which of the considered CNN models turns out to be most accurate under a specific illumination effect. Such an understanding could, therefore, improve the results of detection even when applied to cases of tampered faces in fake Aadhaar detection and fake id detection, thereby clearly bringing out the usefulness of the research in practice. © 2024 IEEE.

关键词： Convolutional neural networks

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CLIMAX: An Exploration of Classifier-Based Contrastive Explanations 5

CLIMAX: An Exploration of Classifier-Based Contrastive Expla...

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5th IEEE International Conference on Cognitive Machine Intelligence, CogMI 2023

作者： Nanavati, Praharsh Prasad, Ranjitha Indian Institute of Science Education and Research Data Science and Engineering Bhopal India Indraprastha Institute of Information Technology Electronics and Communication Engineering Delhi India

ISBN: (纸本)9798350323832

Explainable AI is an evolving area that deals with understanding the decision making of machine learning models so that these models are more transparent, accountable, and understandable for humans. In particular, post-hoc model-agnostic interpretable AI techniques explain the decisions of a black-box ML model for a single instance locally, without the knowledge of the intrinsic nature of the ML model. Despite their simplicity and capability in providing valuable insights, existing approaches fail to deliver consistent and reliable explanations. Moreover, in the context of black-box classifiers, existing approaches justify the predicted class, but these methods do not ensure that the explanation scores strongly differ as compared to those of another class. In this work we propose a novel post-hoc model agnostic XAI technique that provides contrastive explanations justifying the classification of a black box classifier along with a reasoning as to why another class was not predicted. Our method, which we refer to as CLIMAX which is short for Contrastive Label-aware Influence-based Model Agnostic XAI, is based on local classifiers. In order to ensure model fidelity of the explainer, we require the perturbations to be such that it leads to a class-balanced surrogate dataset. Towards this, we employ a label-aware surrogate data generation method based on random oversampling and Gaussian Mixture Model sampling. Further, we propose influence subsampling in order to retaining effective samples and hence ensure sample complexity. We show that we achieve better consistency as compared to baselines such as LIME, BayLIME, and SLIME. We also depict results on textual and image based datasets, where we generate contrastive explanations for any black-box classification model where one is able to only query the class probabilities for an instance of interest. © 2023 IEEE.

关键词： Decision making

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Understanding the Regional Differences in World Happiness Index Using Machine Learning

Understanding the Regional Differences in World Happiness In...

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2023 International Conference on Innovative Computing, Intelligent communication and Smart Electrical Systems, ICSES 2023

作者： Timmapuram, Mayush Ramdas, Rithvik Vutkur, Sadhvik Reddy Mali, Yashwanth Reddy Vasanth, K. Chaitanya Bharathi Institute of Technology Artificial Intelligence and Data Science Hyderabad India Chaitanya Bharathi Institute of Technology Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350319200

The World Happiness Report, created by the Sustainable Development Solutions Network and influenced by Gallup World Poll data, responds to a global call for happiness and well-being to be prioritized as core governance measures. The survey highlights the science of happiness across a region or country by investigating personal and national variances in happiness. This study entails data collection, cleansing, and exploratory analysis to uncover variable correlations. It discovers key factors of national happiness levels through correlation heat map and feature selection. Following that, prediction models based on machine learning techniques, such as regression and decision trees, are developed to anticipate a country's happiness score based on the Quality-of-Life factors relating to social, economic, and health data. The aim of this study is to provide insights into elements that contribute to the happiness of a population and explore the various policies implemented by the governments of progressive countries which led to societal upliftment. The regression techniques employed by the study have performed excellently, with an R-squared value of 0.7867 and a Root Mean Square Error (RMSE) of 0.4797. Notably, the Quality of Life (QoL) indicator for the year 2023 was predicted using Support Vector Regression (SVR), which turned out to be the best approach overall. In a nutshell, this study demonstrates data science's ability to decipher complex societal issues, as seen through the perspective of happiness and well-being. © 2023 IEEE.

关键词： Deep Learning Machine Learning Quality of Life Wellbeing World Happiness Index

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