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AI-Assisted IoT System for Real-Time Electricity theft

AI-Assisted IoT System for Real-Time Electricity Theft

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Pushpalatha D Shobika S Sharmila S Sivapriya S Vinotha P Department of ECE M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Electricity theft is a recurring global problem that prevents effective energy distribution and causes significant financial losses for utility corporations. Conventional detection techniques are inefficient, labor-intensive, and prone to errors since they frequently depend on human procedures. this study presents a novel Internet of things (IoT)-based system for monitoring and detecting electricity theft in real time. Leveraging smart meters and IoT-enabled sensors, the system continuously monitors electricity usage to identify anomalies indicative of unauthorized consumption. Advanced machine learning algorithms analyze collected data to detect theft patterns with high precision. Upon identifying irregularities, the system promptly notifies utility providers and authorities, enabling swift corrective actions. the proposed solution offers a cost- effective and scalable approach for monitoring and securing power distribution networks, significantly reducing revenue losses and improving operational efficiency. Comprehensive simulations and real-world testing validate the system’s reliability, demonstrating its potential to revolutionize electricity theft prevention and enhance the sustainability of energy management systems.

关键词： Electricity Companies Real-time systems sensor systems Smart grids Power system reliability Reliability Internet of things Monitoring Intelligent sensors

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Signal processing and Connectivity Analysis in Calcium Imaging Data: Unveiling Neuronal Network Dynamics 25

Signal Processing and Connectivity Analysis in Calcium Imagi...

引用

Proceedings of the 2025 5th international conference on Bioinformatics and Intelligent Computing

作者： Yuanhang Jia Ruinan Yang Institute of Architecture Tianjin University Tianjin Tianjin China Institute of Biomedical Engineering Sichuan University Chengdu Sichuan China

ISBN: (纸本)9798400712203

Calcium imaging, as a means of high temporal and spatial resolution, has been widely used in neuroscience research to monitor the dynamic activity of neuronal networks. In this paper, a comprehensive analysis process for calcium imaging data is proposed to improve the accuracy and effectiveness of data processing, and the analysis is performed using OASIS. First, Z-score standardization is applied to normalize the signal of each neuron to eliminate the difference in signal amplitude between different neurons. then, we design and implement a high-pass filtering method based on the frequency domain to extract high-frequency components and remove low-frequency noise and background signals by fast Fourier transform (FFT), thus enhancing the signal features of neuronal activity. the method was verified on two sets of calcium imaging data, and the results show that the signal after filtering is clearer and can capture the instantaneous activity and synchronization pattern of neurons more effectively. Finally, the result displays that the highly connected neurons in the primary visual cortex mainly interact with a specific population of neurons, indicating the existence of a specific local network structure. this study provides an effective tool for calcium imaging data analysis in the field of neuroscience and is expected to be widely used in the dynamic study of complex neural networks.

关键词： Calcium imaging

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Integrated IoT-based Hazard Detection and Automated Waste Management System for Environmental Safety

Integrated IoT-based Hazard Detection and Automated Waste Ma...

引用

Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Samyudha K Swethaa Sree R Swetha A Fatima Vincy R Department of Computer Science and Engineering Easwari Engineering College Chennai India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Waste management is a critical challenge in urban environments, where inefficient disposal methods contribute to pollution, public health risks, and operational inefficiencies. Traditional waste collection systems lack real-time monitoring, leading to overfilled bins, delayed disposal, and increased environmental hazards. In this research, an IoT-based smart dustbin system is designed to optimize waste management through automated real-time monitoring. the system integrates an ultrasonic sensor to detect waste levels, an IR sensor for user interaction, and a temperature sensor for fire hazard detection. A key novelty of this research is the incorporation of three MQ gas sensors for toxic gas detection, ensuring early identification of hazardous emissions from waste decomposition. the data is transmitted via a Wi-Fi module to a web-based dashboard, displaying waste levels, gas concentrations, timestamps, and geolocation. Additionally, an automated GSM-based SMS notification system alerts municipal authorities when the dustbin is full or detects hazardous gases. While IoT integration enhances urban sanitation and operational efficiency, challenges such as power consumption and network dependency persist. To address these concerns, our system optimizes data transmission intervals and leverages energy-efficient components. this approach enables seamless waste management, reducing manual monitoring efforts while fostering a cleaner and safer urban environment.

关键词： Waste management Urban areas Data processing Real-time systems Hazards Energy efficiency Data communication Internet of things Monitoring Gas detectors

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Development of a Multi-Dimensional Image processing Model for Personalized Diagnosis and Severity Assessment of Skin Diseases

Development of a Multi-Dimensional Image Processing Model fo...

引用

Advanced Research in Computing (ICARC), international conference on

作者： Nethmi Shihari Wijerama PPG Dinesh Asanka thilini V. Mahanama Department of Industrial Management Faculty of Science University of Kelaniya Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Skin diseases affect millions worldwide, and more than 3,000 different conditions present significant challenges to diagnose and suggest treatments. this study introduces a novel multi-dimensional image processing model that gives three critical outputs: skin type classification, Skin disease prediction, and severity level assessment. Unlike the traditional image-only diagnostic models, this approach puts forward a customized convolutional neural network (CNN) architecture that utilizes high-resolution dermatological images with clinical data, including patient demographics, and symptomatology, to provide a personalized and accurate diagnostic solution. Considering the variation in skin type and disease manifestation, the model has been adapted to adjust its prediction based on the unique characteristics of every patient. the final model, trained on such a diverse and clinically representative dataset, includes the diagnosis based on the severity—(mild, moderate, or severe)—and skin types, such as dry, oily, or normal, which represents critical gaps in current AI applications. the developed model could be implemented in real-time clinical use, as it is scalable and data-driven, effectively addressing diagnostic challenges. It is particularly beneficial in resource-constrained settings, offering accurate and tailored skin disease diagnostics to meet individual patient needs.

关键词： Adaptation models Accuracy Image processing Predictive models Skin Data models Robustness Real-time systems Convolutional neural networks Diseases

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Image processing and CNN-Based Anxiety, Depression, and Hypertension Identification through Iridology: A Systematic Review

Image Processing and CNN-Based Anxiety, Depression, and Hype...

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Advanced Research in Computing (ICARC), international conference on

作者： Kpi Nethtra Dvds Abeysinghe Department of Computer Science Faculty of Computing General Sir John Kotelawala Defence University Ratmalana Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

the extensiveness of stress-related physiological and other psychological conditions such as anxiety and depression requires new diagnostic approaches. this research will aim at finding out the efficiency of using iris analysis in diagnosing such conditions as a complementary to the current methods that are either invasive or rely on the patient’s perception. Based on the interviews with ophthalmologists and psychologists, it was discovered that the movements of the iris and pupil controlled by the autonomic nervous system may contain information about stress and mental health disorders. Ophthalmologists noted that iris could be used as a bio-marker while clinical psychologists noted that speech based assessment was still the common practice in the assessment of mental health. Although iris analysis cannot be used as the sole diagnostic tool, this research shows that it could be a valuable adjunct when combined with standard approaches. the proposed system will employ high quality of iris image capture and the state of the art machine learning especially Convolutional Neural networks (CNNs) to detect abnormalities concerning psychological and physiological disorders. the integration of image processing with real time data analysis is a less invasive and may be earlier diagnostic tool than the conventional methods in non-ocular diseases and enhances the accuracy and speed of diagnosis. this approach is meant to encourage a holistic practice of medicine.

关键词： Hypertension Iris Accuracy Anxiety disorders Mental health Depression Physiology Reproducibility of results Standards Systematic literature review

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Analyzing the Link Lifetime and Doppler Shift Between Connected Vehicles in VANET 5th

Analyzing the Link Lifetime and Doppler Shift Between Connec...

引用

5th international conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2023

作者： Nagar, Vishvendra Pal Singh Sagar, Anil Kumar Department of Computer Science and Engineering Sharda University Greater Noida India Sharda University Greater Noida India

ISBN: (纸本)9789819600465

the highly dynamic new technology known as Vehicular Ad Hoc networks (VANETs) supports wireless communication between hybrid vehicles. Wireless sensor networks’ (WSNs) technology is used by hybrid cars for a range of tasks, including real-time data gathering, monitoring, control, and sharing that data for remote monitoring and system diagnostics. Hybrid vehicles may interface with infrastructure via Intelligent Transport System (ITS) to receive real-time traffic, road, and weather information. Depending on the road's specifications, a vehicle’s mobility speed may vary. Certain VANET characteristics, such as dynamic vehicle speed, vehicle mobility in terms of movement, network density, etc., make wireless communication challenging to maintain the connectivity of dynamic wireless network. As speed is one of the factors that leads to a rapid change in topology, this paper examines and analyzes the effects of speed and mobility on the performance of wireless communication protocols. the findings show that raising a vehicle's speed has an adverse impact on the connection of the network, with a negative correlation between speed and wireless network link lifetime. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mobile ad hoc networks

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Nano-Orifice Based Dielectrophoretic Manipulation and Characterization of Micro-target

Nano-Orifice Based Dielectrophoretic Manipulation and Charac...

引用

sensors and Information Technology (ICSI), international conference on

作者： Wensi Zhang Jia Lou Nan Qin Tiger H Tao Kaihuan Zhang Kai Zhao 2020 X-Lab & the State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China Department of Information Science and Technology Dalian China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

this study introduces an innovative continuous microfluidic DC-dielectrophoresis (DC-DEP) technique for micro/nanoparticle separation using pressure-driven flow. the method employs asymmetric orifices (large-small configuration) on the channel walls to establish a non-uniform electric field through low-voltage application. As particles traverse the high-field-gradient region near the small orifice, differential DEP responses (positive/negative) are induced based on their size and dielectric properties. By optimizing applied potentials and electrolyte conductivity, the system enables effective size-based separation of polystyrene nanoparticles down to nanoscale dimensions. Compared to conventional DC-DEP devices that rely on physical obstacles for field non-uniformity, this design achieves superior performance through localized electric field enhancement without requiring complex microstructures. Key advantages include reduced Joule heating due to localized low-voltage operation (typically <10 V) and extended applicability for nanoparticle processing.

关键词： Nanoparticles Performance evaluation Low voltage Orifices sensor phenomena and characterization Nanoscale devices Microstructure Electric fields Information technology Microfluidics

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Prediction of Wastewater Quality in a Certain Aquaculture Farm Based on Autoformer

Prediction of Wastewater Quality in a Certain Aquaculture Fa...

引用

sensors and Information Technology (ICSI), international conference on

作者： Pengju Zhang School of Computer Science South-Central Minzu University SCMU Wuhan China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

the research and development teams from the School of Computer Science and the School of Resources and Environment of our university have used the BIE (Bio oxidative Information Environment) aerobic microbial enhancement system on a sewage treatment project in a certain area of Hubei province, the use of sensors and automated control technology for online cultivation of microorganisms. However, during the processing of this system, there are problems such as severe manual dependence and delayed acquisition of water quality parameters. In response to the above issues, this article predicts water quality parameters based on an improved model of the Transformer model, the Autoformer model. Firstly, the Autoformer model utilizes autocorrelation mechanisms to achieve efficient sequence level data connections and expand information utility. then, based on the different time points in different cycles, a Time-WeightMAE loss function is designed to reduce the impact of outliers on model training. MAE and MSE are used to evaluate and explore the model by comparing with the Long Short Term Memory Network (LSTM) model, CNN-LSTM model, and Informer model, the MAE and MSE values of the Autoformer model have decreased by 10.8% and 22% respectively compared to the Informer model, which can be effectively applied to microbial culture systems and improve parameter optimization capabilities

关键词： Training Water quality Predictive models Transformers sensor systems Wastewater Wastewater treatment Long short term memory Context modeling Aquaculture

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Capsule networks with a Self-Attention Mechanism for Classification of Parkinson's Disase from Tremor

Capsule Networks with a Self-Attention Mechanism for Classif...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Rongrong Ban School of Information Engineering Guangdong University of Technology Guangzhou China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

the traditional diagnosis of Parkinson's disease (PD)primarily relies on the Unified Parkinson's Disease Rating Scale and the clinical experience of physicians, leading to significant subjectivity in diagnosis. To address this issue, we employ sensor systems to collect tremor parameters from PD patients and analyzed these collected data using traditional machine learning algorithms, providing an objective and quantitative diagnosis for PD. To obtain more comprehensive features, this study adopts the PKG wearable device to extract 12 tremor features from 100 PD patients, along with recording 9 scale-based features and 3 demographic characteristics. Furthermore, to overcome the issues of low accuracy and poor generalization ability of traditional machine learning methods, we propose a capsule network integrated with a self-attention mechanism (SA-CapsNet) for classifying and predicting early stage and moderate to advanced stage stage PD patients. We also utilize the Synthetic Minority Over-sampling Technique (SMOTE) to improve the model's ability to generalize. Lastly, the Shapley Additive exPlanations (SHAP) model is introduced to improve the interpretability of the predictive model, and feature analysis is conducted to identify the main factors influencing classification and prediction. Experimental results demonstrate that the SA-CapsNet method for PD can achieve an accuracy of 92.31%, precision of 82.19%, recall of 91.23%, specificity of 80.00%, and an F1 score of 85.25%. therefore, the proposed SACapsNet method not only exhibits good generalization ability for small sample sizes and imbalanced class data but also effectively assists physicians in staging the severity of PD.

关键词： Support vector machines Accuracy Parkinson's disease Medical services Predictive models Feature extraction sensor systems Data mining Wearable devices Wearable sensors

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Machine Learning Based Enhanced Remote Power Analysis Attacks

Machine Learning Based Enhanced Remote Power Analysis Attack...

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Advanced Research in Computing (ICARC), international conference on

作者： Ishan Kasthuripitiya Jeewantha Ariyawansha tharindu Ranasinghe Manjula Sandirigama Darshana Jayasinghe Department of Computer Engineering University of Peradeniya Peradeniya Sri Lanka School of Electrical and Computer Engineering The University of Sydney Sydney Australia

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Remote Power Analysis (RPA) attacks have demonstrated the ability to reveal secret keys from cryptographic implementations. RPA attacks have been demonstrated on Advanced Encryption Standard (AES) circuits running on Field Programmable Gate Arrays (FPGAs). thus far, RPA attacks were conducted using classical attacks such as correlation power analysis attacks (CPA).this paper presents a comprehensive study on the application of machine learning (ML) algorithms to improve the efficacy of RPA attacks against AES. We explore the integration of Convolutional Neural networks (CNNs) and Multi-Layer Perceptrons (MLPs) within the state-of-the-art AISY framework (initially proposed for classical power analysis attacks) to detect and exploit RPA vulnerabilities in AES-encrypted FPGA systems. the proposed methodology involves capturing power traces using a Time-to-Digital Converter (TDC) on-chip sensor during AES operations and then using MLP and CNN models to conduct RPA attacks to evaluate security. We demonstrate that ML-enhanced RPAs can significantly increase attack success rates while requiring fewer data samples. Our results show that CNN-based RPA attacks can reveal the secret within 10,000 traces. the research underscores the need for ongoing innovation in cryptographic defences to counteract the evolving threat landscape.

关键词： Technological innovation Circuits Machine learning Side-channel attacks Stability analysis System-on-chip Convolutional neural networks Integrated circuit modeling Field programmable gate arrays Standards

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