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Anomaly detection using deep learning approach for IoT smart city applications

Multimedia Tools and Applications 2025年第17期84卷 17929-17949页

作者： Shibu, S. Kirubakaran, S. Remamany, Krishna Priya Ahamed, Suhail Chitra, L. Kshirsagar, Pravin R. Tirth, Vineet Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad501401 India Engineering Department College of Engineereing and Technology University of Technology an Applied Sciences PC Musandam811 Oman Machine Learning Software Engineer Paragon Semvox GmbH Saarland Kirkel Germany Department of Electrical and Electronics Engineering Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Tamil Nadu Chennai603104 India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Maharashtra Nagpur India Department of Mechanical Engineering College of Engineering King Khalid University Asir Abha61421 Saudi Arabia

With the advancements of IoT devices, many smart applications start to rule this era. In particular, smart cities has been adapted and realized by many countries around the world. In smart cities, vas amount of data is generated at every second. This vast data need a transmission medium which could be wireless standard. However, security is the main concern in such applications since the smart transmission always binds with anomalies. The existing anomaly detection systems need improvement in accuracy due to inefficient feature extraction and selection procedure. This paper proposes an accurate anomaly detection technique that built upon deep learning approach. We proposed a Combined Deep Q-Learning (CDQL) algorithm for anomaly detection. Priory, optimal features are selected by using Spider Monkey Optimizer (SMO). With the optimal features, CDQL detects anomalies accurately. In addition, the CDQL algorithm learns the environment in order to monitor the network data continuously. This continuous monitoring and optimum features helps in accuracy improvement up to 98%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications 4

Adaptive Deep Reinforcement Learning-based Resource Manageme...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karne, Niharika Ramagundam, Chandra Shekar Patnala, Ranga Rao Ramagundam, Shashishekhar Tadiboina, Sai Nitisha Sr. Principal Systems Engineer in C&RS Platform Services University of South Alabama Department of Electrical Engineering Masters in Electrical Engineering Amtrak DT United States Software Engineer in Cloud Platform Services City of Philadelphia Pennsylvania United States Vice President Neuberger Berman United States Software Development & Engineering in Content Discovery & AI Troy University Department of Computer Science Masters in Computer Science AL Comcast Montgomery Campus United States Department of Information Technology GEICO - Chevy Chase Chevy ChaseMD20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Deep reinforcement learning

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A Novel High Step-up Switched Capacitor DC-DC Converter Based on Coupled Inductors 4

A Novel High Step-up Switched Capacitor DC-DC Converter Base...

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4th International Conference on Power and Energy Technology, ICPET 2022

作者： Bai, Xuechen Liu, Hao Gu, Jingyun Cai, Hongye Guan, Yueshi Xu, Dianguo University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada Harbin Institute of Technology School of Electrical Engineer and Automation Harbin China

ISBN: (数字)9781665480796

ISBN: (纸本)9781665480796

A novel high step-up switched capacitor DC-DC converter based on coupled inductors is proposed in this paper. The proposed topology solves the problem of insufficient voltage on the switched capacitor, thus improves the traditional energy storage structure. Combined with the coupled inductor, which further improve the voltage gain characteristics and reduce the stress of the switching device. The operating principle and steady state analysis of the converter are introduced in details, and a 200W experimental prototype with an input voltage of 25-45V and an output voltage of 380V is established in the laboratory to verify the feasibility of the converter. © 2022 IEEE.

关键词： Gain measurement

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Mapping Meta-atoms to Near Field Response for Inverse Design of Optical Metasurfaces 8

Mapping Meta-atoms to Near Field Response for Inverse Design...

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Advanced Optics for Imaging Applications: UV through LWIR VIII 2023

作者： Varner, Andy G. Lindsay, Marshall B. Veal, Charlie T. Kovaleski, Scott D. Anderson, Derek T. Price, Stanton R. Price, Steven R. Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO United States U.S. Army Engineer Research and Development Center VicksburgMS United States

ISBN: (数字)9781510661752

ISBN: (纸本)9781510661745

Optical metasurfaces enable devices to interact with light in unique ways by modulating phase, polarization, or intensity. A metasurface, composed of individual subwavelength scatterers known as meta-atoms, can be designed to provide unparalleled control of wavefronts for a variety of optical applications, yet the design of such devices is often unintuitive and challenging due to computationally expensive forward simulations and the number of free parameters. To overcome this, there is interest in developing inverse design methods as an alternative to conventional forward design. Inverse design leverages machine learning algorithms to effectively search a problem space, starting from application and resulting in solution parameters. In this work, we adopt an inverse design approach that involves targeted forward simulations of arbitrary meta-atoms. To ensure that the dataset captures all possible shapes and rotations of near field responses with second order accuracy, it is constructed using meta-atoms with varying geometries and corresponding phase shifts, including the effect of nearest neighbors. A custom deep learning system is developed to extract meaningful features from this near field response. The proposed framework provides flexibility to produce an inverse design paradigm for generalized metasurface applications without the need for repeated forward simulations. Additionally, the machine learning model is highly effective in reconstructing electric fields, irrespective of the loss function used. © 2023 SPIE.

关键词： Atoms

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Impact of Data Variety on Physics-informed Neural Network Lens Design 8

Impact of Data Variety on Physics-informed Neural Network Le...

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Advanced Optics for Imaging Applications: UV through LWIR VIII 2023

作者： Lindsay, Marshall B. Kovaleski, Scott D. Varner, Andy G. Veal, Charlie Weber, Justice Anderson, Derek T. Price, Stanton R. Price, Steven R. Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO United States U.S. Army Engineer Research and Development Center VicksburgMS United States

ISBN: (数字)9781510661752

ISBN: (纸本)9781510661745

The application of neural networks in optical component design has witnessed rapid growth in recent years. This design framework encodes the numerical simulation of optical wave propagation and material wave modulation directly within neural network layers, enabling the optimization of physical quantities, such as transmissivity values of diffractive optical elements, through gradient descent and backpropagation algorithms. Physics-informed neural networks have been employed in designing diffractive deep neural networks, optimizing holograms for near-eye displays and creating multi-objective traditional optics. However, there remains a lack of evaluation for training parameters, and discrete sampling considerations are often overlooked. To address these gaps, this study examines the impact of dataset variety on physics-informed neural networks in optimizing lenses that either satisfy or violate the Nyquist sampling criteria. Results show that increased data variety enhances optimized lens performance across all cases. Optimized lenses demonstrate improved imaging performance by reducing diffraction orders present in aliased analytical lenses. Moreover, we reveal that low data variety leads to overfit lenses that function as selective imagers, providing valuable insights for future lens design and optimization. © 2023 SPIE.

关键词： Optimization

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Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks 5

Deception Detection from Linguistic and Physiological Data S...

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5th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2024

作者： Li, Panfeng Abouelenien, Mohamed Mihalcea, Rada Ding, Zhicheng Yang, Qikai Zhou, Yiming University of Michigan Department of Electrical and Computer Engineering Ann Arbor United States University of Michigan Department of Computer Science Ann Arbor United States Columbia University Fu Foundation School of Engineering and Applied Science New York United States University of Illinois Urbana-Champaign Department of Computer Science Urbana United States Saarland University of Applied Science Department of Engineer Sciences Saarland Germany

ISBN: (纸本)9798350352719

Deception detection is gaining increasing interest due to ethical and security concerns. This paper explores the application of convolutional neural networks for the purpose of multimodal deception detection. We use a dataset built by interviewing 104 subjects about two topics, with one truthful and one falsified response from each subject about each topic. In particular, we make three main contributions. First, we extract linguistic and physiological features from this data to train and construct the neural network models. Second, we propose a fused convolutional neural network model using both modalities in order to achieve an improved overall performance. Third, we compare our new approach with earlier methods designed for multimodal deception detection. We find that our system outperforms regular classification methods;our results indicate the feasibility of using neural networks for deception detection even in the presence of limited amounts of data. © 2024 IEEE.

关键词： Convolutional neural networks

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Diabetes Classification Problem with CatBoost Method and Optuna Gradient Boosting Optimization 6

Diabetes Classification Problem with CatBoost Method and Opt...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Kurniawan, Safar Dwi Purwono, Purwono Ma'arif, Alfian Suwarno, Iswanto Politeknik Harapan Bersama Department of Computer Engineering Tegal Indonesia Universitas Harapan Bangsa Department of Informatics Purwokerto Indonesia Universitas Ahmad Dahlan Department of Electrical Engineering Yogyakarta Indonesia Universitas Muhammadiyah Yogyakarta Department of Engineer Professional Program Yogyakarta Indonesia

ISBN: (纸本)9798350315639

Diabetes mellitus is a dangerous global epidemic that can cause kidney failure, heart attack, blindness, and death. The main cause of this disease is too high blood sugar levels due to the irrelevant work of pancreatic beta cells. Rapid computer technology advances and implementations based on artificial intelligence have penetrated various fields, especially the health sector. Data mining and machine learning are technologies that continue to grow, are reliable, and can be used as research support tools in the medical field. This technology is considered capable of helping to automate diabetes prediction. Based on related research, our contribution to this research is to utilize the CatBoost method with Optuna optimization. In the data preprocessing stage, all 768 data sets have gone through removing outliers, normalizing features to reducing dimensions. The dataset is divided into 85\% composition for training data and 15\% for test data. At the feature selection stage with Correlation Pearson's, we found that Glucose correlated with diabetes outcome or disease because it had a value range of 0.47. The results' accuracy is considered less than optimal because it only produces an accuracy value below 80%, even after optimization with Optuna. It can be seen that there was only an increase of 7\% after optimizing with Optuna. © 2023 IEEE.

关键词： Data mining

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Optimizing MEMS Optical Arrays with Silicon Micro Mirrors using Machine Learning Techniques 3

Optimizing MEMS Optical Arrays with Silicon Micro Mirrors us...

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3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Joshi, Vilas Ramrao Sharma, Surender Kumar Galande, Vijay Agatrao Sachin Patil, Pallavi Patil, Ushadevi Suttarwala, Hussain Sharma, Deepak K. Savitribai Phule Pune University Department of Computer Engineering Pune India Poornima University Department of Electrical Engineering Jaipur India KJEI's Trinity Academy of Engineering Department of Engineering Science Pune India Genba Sopanrao Moze College of Engineering Department of Civil Engineering Pune India American Express Senior Software Engineer PhoenixAZ85054 United States University of Technology Jaipur India

ISBN: (纸本)9798350361391

In the rapidly evolving landscape of optical microsystems, the infusion of Machine Learning (ML) techniques stands out as a catalyst for unprecedented progress. This research paper delves into the intricate interplay between Micro-Electro-Mechanical Systems (MEMS), optical arrays, and silicon micro mirrors, shedding light on the transformative prowess unleashed by Machine Learning in optimizing these intricate components. Focusing on the limitations posed by conventional designs, the paper not only illuminates the challenges encountered but also presents a compelling narrative of how ML algorithms emerge as formidable allies, poised to surmount these hurdles with ingenuity and precision. The exploration extends beyond a mere technical discourse;it unfolds as a narrative of innovation, where the convergence of MEMS, optical arrays, and silicon micro mirrors, guided by the intelligence of Machine Learning, transcends traditional boundaries, propelling optical microsystems into an era of unparalleled efficiency and adaptability. This research beckons a new epoch, where the intricate dance of silicon micro mirrors and the intricate mechanics of MEMS find harmony through the symphony of Machine Learning, reshaping the landscape of optical microsystems and inspiring future possibilities that were once deemed beyond reach. © 2024 IEEE.

关键词： Microchannels

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A Secure Hybrid Permissioned Blockchain and Deep Learning Platform for CT Image Classification 12

A Secure Hybrid Permissioned Blockchain and Deep Learning Pl...

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12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022

作者： Noei, Mohammadreza Parvizimosaed, Mohammadreza Saleh Bigdeli, Aliakbar Yalpanian, Mohammadmostafa Tarbiat Modares University Department of Electrical and Computer Engineering Tehran Iran K. N. Toosi University of Technology Department Computer Engineering Tehran Iran TisEnergy Network Engineer Tehran Iran

ISBN: (纸本)9781665412162

Pneumonia is a life-threatening and prevalent disease and needs to be diagnosed within a short time because of the lungs' fluid flow. Late detection of the disease may result in the patient's death. Thus, advanced diagnosis is a critical factor besides the disease progress. In addition to advanced diagnosis, the privacy of datasets is important for organizations. Due to the great value of datasets, hospitals do not want to share their datasets, but they want to share their trained network weights. Therefore, in this paper, we combine deep learning and blockchain to implement the blockchain as distributed storage. Using permission blockchain, weights are broadcasted among other hospitals securely. Because of the security, the dataset is shared with five hospitals equally. Each hospital trains its network model and sends its weights to the blockchain. The goal is to broadcast the aggregated weights among hospitals securely and have good enough results because the whole dataset is not implemented to train a network. The dataset contains 5856 images, and hospitals implement a residual neural network with 28 layers. The results show that hospitals can increase the accuracy of their model using shared weights compared to a model without using shared weights. © 2022 IEEE.

关键词： Blockchain

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A new hybrid control technique for operation of DC microgrid under islanded operating mode

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Protection and Control of Modern Power Systems 2022年第1期7卷 646-656页

作者： Vahid Mortezapour Sajjad Golshannavaz Edris Pouresmaeil Amin Yazdaninejadi School of Electrical and Computer Engineering University of Tehran16th Azar St.Enghelab Sq.TehranIran Electrical Engineering Department Urmia University11Km SERO RoadUrmia CityIran Department of Electrical Engineering and Automation Aalto UniversityAalto University Foundation srPO BOX 11000FI-00076 AaltoFinland Department of Electrical Engineer-ing Shahid Rajaee Teacher Training UniversityShabanlouQFJR+2MPTehranTehran ProvinceIran.

This study proposes a novel combined primary and secondary control approach for direct current microgrids,specifi-cally in islanded *** primary control,this approach establishes an appropriate load power sharing between the distributed energy resources based on their rated ***,it considers the load voltage deviation and provides satisfactory voltage regulation in the secondary control *** proposed primary control is based on an efficient droop mechanism that only deploys the local variable measurements,so as to overcome the side effects caused by communication *** the case of secondary control,two different methods are *** the first,low bandwidth communication links are used to establish the minimum required data transfer between the *** effect of communication delay is further *** second method excludes any communication link and only uses local ***,a self-sufficient control loop is devised without any communication *** proposed control notions are investigated in MATLAB/Simulink platform to highlight system *** results demonstrate that both proposed approaches can effectively compensate for the voltage deviation due to the primary control *** comparisons of the two methods are also provided.

关键词： Hybrid control technique Modern power system DC microgrid Islanded operating mode

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