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Cognitive Twins for Predictive Maintenance and Security in IoT Software Systems

Cognitive Twins for Predictive Maintenance and Security in I...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： B. Venkata Swamy Pareshwar Prasad Barmola Senthil thangavel S. Kaliappan Hardik Patel Girish Abhyankar Department of Chemistry B V Raju Institute of Technology Medak India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Distributed Systems Cloud Solutions & Machine Learning Expert Paypal Inc California USA Division of Research and Development Lovely Professional University Phagwara India Department of Electronics & Communication Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda India Faculty of Law Symbiosis Law School Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

the increasing complexity and interconnectedness of Internet of things (IoT) software systems necessitate the development of intelligent solutions for predictive maintenance and security. Conventional techniques often fail to provide real-time insights and proactive responses due to the diverse and dynamic nature of IoT environments. To address these challenges, cognitive technologies offer promising avenues for enhancing the operational efficiency and security of IoT networks. this paper introduces Cognitive Twins, an AI-driven framework designed to optimize predictive maintenance and strengthen security in IoT software systems. Cognitive Twins leverage advanced machine learning models and real-time data streams to create dynamic digital replicas of IoT devices and software components. the framework employs a combination of deep learning-based anomaly detection, reinforcement learning for proactive maintenance scheduling, and natural language processing (NLP) for automated security log analysis. By continuously learning from device interactions and evolving threat patterns, Cognitive Twins predict potential failures and detect security threats before they occur, enabling real-time decision-making and automated responses. Cognitive Twins were evaluated on a large-scale IoT network, consisting of 500 nodes across multiple application domains. the framework achieved a predictive maintenance accuracy of 96.8%, reducing downtime by 35% compared to traditional models. In security applications, Cognitive Twins identified cyber threats with a detection rate of 98.3%, lowering the false positive rate to 1.5%.

关键词： Biological system modeling Decision making Reinforcement learning Software systems Real-time systems Natural language processing Maintenance Internet of things Security Predictive maintenance

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Vehicle Traffic Estimation Using Deep learning 35th

Vehicle Traffic Estimation Using Deep Learning

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35th international Florida Artificial Intelligence Research Society conference, FLAIRS-35 2022

作者： Patel, Meetkumar Silver, Daniel L. Jodrey School of Computer Science Acadia University WolfvilleNSB4P 2R6 Canada

For commuters, vehicular traffic is an important planning concern. People have access to the weather forecast and the current traffic situation, but there is no application available to estimate traffic congestion and flow in the near future. thus, we design and develop a machine learning approach which can predict vehicular traffic density and flowrate up to two days in the future based on the weather, calendar and special events data. First, Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) networks are utilized to predict the number of new vehicles and the total number of vehicles in images captured by a Nova Scotia Webcams (NS Webcams) video camera. the best models provide a Mean Absolute Percentage Error (MAPE) of 20.38% for the number of new vehicles and 18.56% for the total number of vehicles. these values are used to estimate traffic flowrate and density for hourly records over a three-month period. the hourly traffic data is combined with observed and forecasted weather data, and special event data to create a time series data. A Multiple Task learning (MTL) - LSTM model is trained and tested using these data and a K-fold cross-validation approach. the Mean Absolute Error (MAE) and MAPE are used to evaluate the model performance. the MTL-LSTM model achieves a MAPE of 19.35% and 27.50% for flowrate and density using observed weather data, respectively. In the case of forecasted weather data, the MAPE for flowrate and density increases to 20.51% and 31.10%, respectively. © 2022 by the authors. All rights reserved.

关键词： Long short-term memory

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4th international Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2023, held as part of the 45th European conference on Information Retrieval, ECIR 2023

4th International Workshop on Algorithmic Bias in Search and...

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4th international Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2023, held as part of the 45th European conference on Information Retrieval, ECIR 2023

ISBN: (纸本)9783031372483

the proceedings contain 14 papers. the special focus in this conference is on Algorithmic Bias in Search and Recommendation. the topics include: Understanding Search Behavior Bias in Wikipedia;Do You MIND? Reflections on the MIND Dataset for Research on Diversity in News Recommendations;detecting and Measuring Social Bias of Arabic Generative Models in the Context of Search and Recommendation;what Are We Missing in Algorithmic Fairness? Discussing Open Challenges for Fairness Analysis in User Profiling with Graph Neural Networks;measuring Bias in Multimodal Models: Multimodal Composite Association Score;evaluating Fairness Metrics;utilizing Implicit Feedback for User Mainstreaminess Evaluation and Bias Detection in Recommender Systems;preserving Utility in Fair Top-k Ranking with Intersectional Bias;mitigating Position Bias in Hotels Recommender Systems;improving Recommender System Diversity with Variational Autoencoders;addressing Biases in the Texts Using an End-to-End Pipeline Approach;bootless application of Greedy Re-ranking Algorithms in Fair Neural Team Formation.

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Identification Of Medicinal Plants from Leaf Images

Identification Of Medicinal Plants from Leaf Images

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Global conference for Advancement in Technology (GCAT)

作者： Parth Jhunjhunwala Riya Sanjay Dhawas Shristi Tiwari Abhishek Sharma Electronics and Communication International Institute of Information Technology Naya Raipur India Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur India International Institute of Information Technology Naya Raipur India

Recognition quest for medicinal herbs is a continuous field of exploitation and is crucial in Ayurveda’s industries. Incorrect identification leads to adverse effects. One of the most entrusted classification tools is Support Vector machine (SVM), which deals with high dimensional feature space and is generally a crucial aspect of image data. this paper proposes a comparison of a novel multi-class prediction to segregate plant leaves based on an SVM classifier and deep learning based on Resnet50 and InceptionV3 for leaf classification implemented, each model was individually trained before comparison. the findings from the trained model can be integrated into a web application. the SVM model was built in four stages. Stage 1 involves data preprocessing using techniques like grayscaling, smoothing, thresholding, morphological transformation, and boundary extraction to make the data suitable for SVM-based classification. In Stage 2, shape-based, colour-based, and texture-based features are extracted, and a comma-separated delimiter file is generated that would be fitted in an SVM classification model. Stage 3 and 4 is the model-building and testing stage wherein classification, feature scaling, and parameter tuning is done to obtain optimal hyperparameters for the SVM classifier. Using the transfer learning approach parameters were initialized on the pre-trained Neural networks namely InceptionV3, and ResNet50. When training the models on the relevant dataset, the softmax layer coupled to the dense layer served as the classifier and was used to extract features from the input photos. the last stage involves integrating the solution into a web application. the real-time leaf images can be captured by the user utilizing a camera to obtain plant names from images and upload them to a web interface. the whole framework is compared with the existing frameworks for plant leaf classification.

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Enhancing Road Safety in Diminished Vision Conditions: A Deep learning Approach Using Faster MobileNet for Real-time Object Detection

Enhancing Road Safety in Diminished Vision Conditions: A Dee...

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Intelligent Technologies (CONIT), international conference on

作者： D. Venkata Subbaiah Byreddi Sricharan Shaik Zeenath Aman Kasarla Gopinath Department of Computer Science and Engineering Lakireddy Bali Reddy College of Engineering (Autonomous) Mylavaram India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Despite significant enhancements in car safety technology, traffic accidents still happen, so finding creative ways to reduce the risk is necessary, particularly in situations with poor vision, like fog. this study presents a unique method using deep learning, namely the Faster MobileNet model, to reduce traffic accidents in situations where eyesight is impaired. the suggested solution makes use of a live video feed from the car’s front-mounted camera and Faster MobileNet to identify and highlight things on the driver’s display instantly. To improve image quality, the system incorporates image processing methods such as data augmentation, normalization, and scaling. Convolutional and pooling layers are used in feature extraction, together with a Region Proposal Network (RPN) to find potential object areas. Classifiers are used in object detection to recognize different kinds of objects and give the driver pertinent information. Non-Maximum Suppression (NMS) is incorporated into the implementation as a post-processing step to eliminate duplicate detections and improve the result. By acting as an intelligent helper, the created application greatly increases poor vision drivers’ visibility and promotes a safer driving environment.

关键词： Deep learning Object detection Real-time systems Road safety Safety Proposals Object recognition

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machine learning Models for Micro-bubble Image Detection in Mosquito Sprayer Quality Control:Addressing Class and Scale Imbalance

Machine Learning Models for Micro-bubble Image Detection in ...

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Big Data Analytics and Practices (IBDAP), international conference on

作者： Suppasit Srisaeng Pongsakorn Sadakorn Kritchavat Ploddi Darin Areechokechai Worasait Suwannik Department of Disease Control Ministry of Public Health Thailand Department of Computer Science Kasetsart University

Introduction: Vector-borne diseases (VBDs) account for around 17% of global infectious diseases, with malaria and dengue being the most prevalent. Effective VBD prevention involves controlling mosquito populations by spraying micro-bubble (MB) insecticide. Quality control measures for MB size are essential, but few centers can assess MB size measurement. machine learning (ML) is a powerful tool for automating and enhancing image analysis tasks, including measuring MB size. In this study, we train and evaluate ML models using an MB image dataset to assess their ***: Our experimental study used an MB image dataset labeled by specialists, with images resized and randomly assigned to train, validate, and test datasets. We employed various evaluation metrics, such as recall, precision, and mean average precision (mAP). Image augmentation techniques were applied to increase the number of unqualified MBs. Pretrained models from Detectron2 (D2), MMDetection (MM), and YOLO libraries were used for training and ***: We identified severe class and scale imbalances in the dataset. Single-stage and two-stage CNN models demonstrated superior performance in MB detection compared to DCN and transformer-based models. D2 outperformed MM, and training Faster-RCNN R-50 on an augmented dataset improved performance for unqualified MB detection. Considering multiple evaluation metrics, YOLO v7 exhibited the highest accuracy and effectiveness in detecting ***: Our study revealed the effectiveness of CNN models in MB detection and the importance of selecting appropriate libraries and pretrained weights. Image augmentation improved unqualified MB detection while considering multiple evaluation metrics provided a more comprehensive understanding of model performance. Models like YOLO v7 and Faster-RCNN R-50 and X-101 are recommended for practical MB quality assessment tasks, as they demonstrate high precision and recall performance for qualified and un

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Risk Level Prediction of Diabetic Retinopathy based on Retinal Images using Deep learning Algorithm 4th

Risk Level Prediction of Diabetic Retinopathy based on Retin...

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4th international conference on Innovative Data Communication Technologies and application, ICIDCA 2022

作者： Meruva, Sai Kiran Reddy Tulasi, Venkata Guru Sukesh Vinnakota, Nithin Bhavana, V. Department of Electronics and Communications Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

Diabetic Retinopathy (DR) is an eye illness related with constant diabetes. Diabetes has turned into a worldwide pandemic, with 365 million individuals anticipated to be impacted by 2025. Subsequently, diabetic retinopathy is the most well-known reason for visual deficiency in the United States. the present world is industrialized, and medicines might be compelling, if we get the disease in early stage. Vision is protected, and it is impressively decreased to cripple visual impairment. We are creating and assessing a strategy as a feature of this study for modern retinopathy findings. this work incorporates arranging Diabetic Retinopathy (DR) into two classes (DR, No-DR) and five unique classes (Mild, Moderate, No-DR, Severe, Proliferate) too. Additionally, Data Collection, Data Analyzing, Data Visualization and Data Pre-handling of the gathered information is finished. this work presents an outline of a deep learning algorithmic methodology and gives execution results to a dataset of 5000 Fundus images. While listing the sickness into two unique classes, a total exhibition rate of 89% correct findings is accomplished utilizing VGG-19 deep learning model which is more precise than VGG-16 and CNN model predictions and while characterizing the infection into five unique classes a total exhibition of 90% correct determination is accomplished utilizing VGG-16 deep learning model which is more accurate than VGG-19 and CNN model calculations. © 2023 the Authors. Published by Elsevier B.V.

关键词： Image classification

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Hidden Markov Model Approach to Predict Performance Improvement of 4G Cellular Network

Hidden Markov Model Approach to Predict Performance Improvem...

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international conference on Electrical Engineering, computer Science and Informatics (EECSI)

作者： Eka Kosasih Tanty Oktavia Information System Management Department BINUS Graduate Program Bina Nusantara University Indonesia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

the automation of performance optimization in 4G cellular networks remains a significant research challenge. Although advances in artificial intelligence, machine learning, and deep learning have been made, their application in cellular networks is still in its early stages. the evolution of automated learning algorithms in these networks requires continuous optimization to improve performance. this research aims to develop a data analysis model using a machine learning approach to predict 4G network Key Performance Indicators (KPIs) and enable automated assessments. the study employs Hidden Markov Models (HMM) to predict KPIs such as eRAB Drop Rate and Setup Success Rate. Data were collected from 1,600 newly deployed sites of a mobile operator in Indonesia over six months, from July 2023 to January 2024. the findings indicate that HMM models can significantly contribute to understanding and enhancing cellular network performance. For instance, the model achieved an accuracy of 99.52% and an F1 score of 99.76% in predicting the eRAB Drop Rate. the Inter-Frequency Handover Success Rate showed 96.15% accuracy and a 97.99 % F1 score, while the RRC Connection Setup Success Rate achieved 91.35% accuracy and a 95.29% F1 score. these metrics highlight the model's effectiveness in predicting KPIs. By utilizing HMM models for high-accuracy KPI analysis, network operators can gain deeper insights into network performance and implement targeted improvements. this approach presents a promising avenue for automating and enhancing performance in 4G networks.

关键词： Cellular networks Support vector machines Analytical models Accuracy Key performance indicator Hidden Markov models Predictive models Prediction algorithms Data models Optimization

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An Optimized Equivariant Quantum Network for Ingenious Building Monitoring and Control using an Android application

An Optimized Equivariant Quantum Network for Ingenious Build...

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Sustainable Expert Systems (ICSES), international conference on

作者： S Tulasi Prasad Sarishma G. Sunitha Rekha Daxa Vekariya Harshal Patil Ramya Maranan Department of Computer Science & Engineering Potti Sriramulu Chalavadi Mallikarjuna Rao College of Engineering & Technology Vijayawada Andhra Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering (Cyber Security) CVR College of Engineering Hyderabad Telangana India Department of Computer Science & Engineering Parul institute of Engineering and Technology Parul University Post Limda Gujarat India School of Computer Science & Engineering IILM University Greater Noida Uttar Pradesh India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

For enhancing the security and functionality of smart environments, motion recognition, classification and intruder detection has to be added into the building monitoring and control system. this research developed a Self-Guided Efficient Net UNet based Equivariant Quantum Network with Alpine Skiing Optimization (SGENUN-EQN-ASO) for improving intruder detection in buildings by utilizing an Android application for real-time motion detection and categorization. To prepare the data for analysis, processing can be done on the Self-Guided Filtering (SGF). An enhanced approach is Essential Objectness, Cascade Networks, Equivariant Quantum Convolutional Neural Networks (EquivQCNN) to enhance the detection precision and EfficientNet-based EE-UNet for precise motion assessment. thus, the Android application works as monitoring and control interface providing real-time notifications and the answers to detected intrusions. With the use of optimisation techniques like Alpine Skiing Optimisation (ASO), which is used to fine-tune the hyperparameters of Equivariant Quantum Neural Networks (EQNNs), the system is able to provide stable and flexible performance. the results demonstrating that the integration of the latest machine learning approaches into the solutions in smart buildings brings significant enhancement to the detection effectiveness and response times, as to the overall security and management.

关键词： Filtering Buildings Control systems Real-time systems Motion detection Security Quantum networks Convolutional neural networks Optimization Monitoring

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Research on the application of computer Aided Corrective Feedback in Foreign Language Grammar Teaching 1

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proceedings of the 4th international conference on Neural Computing for Advanced applications, NCAA 2023

作者： Xu, Jie School of Foreign Languages Chaohu University Hefei238024 China Centre for Digital Humanities and Intercultural Communication Chaohu University Hefei238024 China

ISBN: (数字)9789819958474

ISBN: (纸本)9789819958467

this paper aims to explore the effective mode of computer-assisted corrective feedback applied to foreign language teaching. through experimental design, it focuses on the corrective effects of different feedback methods, including immediate effects and delayed effects. the results of statistical analysis show that written corrective feedback is generally effective, indicating that corrective feedback as a teaching method can promote foreign language learning;and that the effect of metalinguistic corrective feedback is the best, indicating that the effect of corrective feedback is basically positively correlated the degree of dominance of the feedback method;and that the effect of test time on the effectiveness of corrective feedback is negatively correlated with the degree of dominance of the feedback method. the methods and findings of this study can provide a reference for further research on second language grammar acquisition in the computer communication environment. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer programming

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