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Optimal Load Prediction in a Smart Metering Network using Random Forest Algorithm

Optimal Load Prediction in a Smart Metering Network using Ra...

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Power, Control and computing Technologies (ICPC2T), conference on

作者： Krishna Pavan Inala Kottu Krishna Teja Kallem Sreenidhi Reddy Kalakoti Archana Department of Electronics and Communication B V Raju Institute of Technology

Smart meters are electronic devices used to measure and record real-time energy consumption. In this research, we utilized energy consumption data gathered from residential consumers in London, England. Our aim is to leverage various machine learning algorithms to establish connections between historical energy consumption data, allowing us to create and assess short-term load forecasts with low Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). This research benefits customers by enabling them to plan their energy usage effectively, resulting in reduced overall energy bills. Our findings indicate that, among the tested algorithms, Random Forest stands out as the optimal choice, delivering the lowest RMSE and MAPE.

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Development of a Multimodal Machine Learning Model for Seizure Detection Using Wearable devices

Development of a Multimodal Machine Learning Model for Seizu...

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Advances in computing, Communication and Networking (ICAC2N), International conference on

作者： Saurabh Pratap Singh Rathore Sumedha Magotra Sruthy K v Sharma Sonu Kumar Gaganpreet Kaur Sailendra Singh Dept. of Management ICAPSR New Delhi India Dept. of Computer Science Engineering Chandigarh University Chandigarh India Dept. of Computer Science & Engineering Dhanalakshmi Srinivasan College Coimbatore India Dept. of Comp. Eng & Applications Mangalayatan University Aligarh India Lovely Professional University Punjab India Dept. of Computer Science J.S. University Shikohabad India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

This research explores the transformative potential of wearable devices designed to automatically detect & predict epileptic seizures, offering continuous monitoring & early detection capabilities. The study focuses on developing a machine learning model tailored for seizure detection in such devices, leveraging multimodal sensors such as Electrodermal Activity (EDA) & Accelerometer (ACC). By measuring skin resistance and identifying irregular heartbeats, which are signs of upcoming seizures, these sensors allow for precise seizure detection. The study intends to demonstrate the effectiveness of this machine learning model in wearable technology with the goal of improving patient outcomes and seizure management.

关键词： Performance evaluation Support vector machines Logistic regression Accuracy Machine learning algorithms Machine learning Skin Classification algorithms Wearable devices Usability

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High-Performance Carbon Nanotube Optoelectronic Transistors for Memory Applications

High-Performance Carbon Nanotube Optoelectronic Transistors ...

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International conference on Solid-State and Integrated Circuit Technology

作者： Shuang Liu Heyi Huang Yanqing Li Yadong Zhang Feixiong Wang Yupeng Lu Renjie Jiang Jiali Huo Huaxiang Yin Key Laboratory of Fabrication Technologies for Integrated Circuits Chinese Academy of Sciences Beijing Institute of Microelectronics Chinese Academy of Sciences Beijing China School of Integrated Circuits University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350361834

ISBN: (纸本)9798350361841

Semiconducting single-walled carbon nanotubes (SWCNTs), distinguished by their direct bandgap and one-dimensional (1D) architecture, are exceptionally well-suited for crafting nanoscale optoelectronic devices. In our study, we successfully fabricated high-performance carbon nanotube field-effect transistors (CNFETs) that exhibited a subthreshold swing as low as 129 mv/dec and an ON/OFF current ratio exceeding $10^{5}$ . These devices boasted an on-state current surpassing 100 μA/μm, coupled with an ON/OFF current ratio over $10^devices$ at a drain voltage of -1 v. Moreover, we demonstrated the capability of these devices for optical sensing and their capacity to achieve multi-bit memory, underscoring their potential in the burgeoning domain of in-memory computing. We further exhibited the proficiency of CNT optoelectronic transistors in executing multiple memory write and erase. These capabilities lay a robust foundation for the future multifunctional integration of these devices in advanced integrated circuit systems.

关键词： Performance evaluation photonic band gap voltage Carbon nanotubes In-memory computing Rendering (computer graphics) Optoelectronic devices CNTFETs Transistors Optical sensors

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Domain Based Network Intrusion Detection System For Iot 7

Domain Based Network Intrusion Detection System For Iot

引用

7th IEEE conference on Information and Communication Technology, CICT 2023

作者： Madhumithaa, v. Govindarajan, J. Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering 641112 India

ISBN: (纸本)9798350305173

Ensuring security of computer networks is crucial in today's interconnected world. By identifying and reducing security risks, intrusion detection systems (IDS) play a crucial part in protecting network infrastructures. While machine learning (ML) approaches have improved IDS accuracy, there is a lack of generalized frameworks that can be implemented across different datasets, hindering practical deployment. Additionally, the disconnect between ML models and domain knowledge has reduced the explainability of IDS systems. To address these gaps, this research work designed an automated IDS framework that enhances scalability and interpretability along with real time inference. The framework focuses on automation of preprocessing, feature selection and the domain feature extraction using different algorithms. Additionally, edge deployment in improving intrusion detection for IoT devices. The results are evaluated using five Machine Learning models and contrasted using an automated and a manual feature selection procedure for each of the 10 attacks. The system concentrates on three categories of assaults for deployment purposes: Distributed Denial of Service (DDoS), Denial of Service (DoS), and normal traffic. To classify these assaults, a logistic regression model is trained and the prediction is displayed using Streamlit UI. © 2023 IEEE.

关键词： Intrusion detection

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Atrial Fibrillation Classification: A Hybrid Approach Integrating Support vector Machines and Feature Selection for Reduced Computational Complexity

Atrial Fibrillation Classification: A Hybrid Approach Integr...

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Applied Artificial Intelligence and computing (ICAAIC), International conference on

作者： vanditha Dsouza K. v Keerthana v Francis Densil Raj Dept. of Advanced Computing St. Joseph’s University Bangalore

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Atrial Fibrillation (AFib) is an important cardiac arrhythmia marked by an irregular and frequently accelerated heartbeat which increases the risk of stroke and other cardiovascular complications. Detecting AFib promptly allows for timely intervention along with the treatments like Cardioversion therapy, medications and Surgery or catheter procedures might help mitigate these risks. Wearable devices and mobile health applications make use of single-lead ECG, allowing for immediate identification and response but most of the devices are based on high computation algorithms leading to a rise in the overall cost of devices. The proposed methodology automatically classifies ECG signals into AFib and Normal using Machine Learning algorithm. To increase the diversity in the dataset, this study has considered 5822 ECG signals from two prominent databases, steps like feature engineering based on variable Clustering were used to handle multicollinearity. Different Machine Learning (ML) models were assessed, encompassing Random Forest, Logistic Regression, SvM, and Adaboost. Among these, SvM emerged as the preferred choice due to its outstanding performance, achieving a F1 score of 0.95. The reliability of this evaluation was confirmed through a 10-fold cross validation method and hypertuning the parameters using the Grid Search method. This study can bridge the gap in Atrial Fibrillation diagnosis and potentially revolutionize healthcare by offering trustworthy and accessible diagnostic procedures.

关键词： Support vector machines Machine learning algorithms Accuracy Computational modeling Atrial fibrillation Surgery Electrocardiography

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Comprehensive Survey on Internet of Medical Things (IoMT) - Applications and Challenges

Comprehensive Survey on Internet of Medical Things (IoMT) - ...

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International conference on computing and Networking Technology (ICCNT)

作者： v Sai Anusha R Kiran Kumar G Charan Kumar Palagiri Mabjan Department of Electronics and Communication Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of Computer Applications Annamacharya University Rajampet India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The Internet of Medical Things (IoMT) is changing healthcare through the use of the Internet to connect medical devices, apps, and systems. This helps improve patient care, makes healthcare more efficient, and improves health outcomes. This paper deals with the edge-fog-cloud computing architectures for Internet of Health applications. Remote patient monitoring is done by collecting the data from wearable health devices, processing at different communication levels, applying intelligent algorithms and providing insights to the patient and healthcare professionals. This paper reviews the IoMT architecture and how the entire workflow is managed at device, edge, fog, and cloud levels. The paper also discusses IoMT challenges, such as data security and privacy concerns due to the interconnectedness and interoperability of devices at each layer and hurdles for seamless data integration within healthcare networks.

关键词： Surveys Patient monitoring Data privacy Medical devices Reviews Data security Data integration Internet of Medical Things Computer architecture Interoperability

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Deep Unified Model for Face Recognition based on Convolution Neural Network and Edge computing 8

Deep Unified Model for Face Recognition based on Convolution...

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8th International conference on Communication and Electronics Systems, ICCES 2023

作者： Senthilkumar, v. Saranya, P. Rani, B Kezia Srinivasan, P. Kuchipudi, Ramu Walid, Md. Abul Ala Kumaraguru College of Technology Department of Cse Tamilnadu Coimbatore India Dr. N.G.P. Institute of Technology Department of Ece Tamilnadu Coimbatore India Vasavi College of Engineering Department of Information Technology Telangana Hyderabad India Sona College of Technology Department of Ece Tamilnadu Salem India Chaitanya Bharathi Institute of Technology Department of Information Technology Telangana Hyderabad India Bangladesh

ISBN: (纸本)9798350396638

CCTv, communication, and alarm systems use face recognition technologies. Face detection in photos is a popular topic in science for practical reasons and because it challenges computer-generated vision systems. The variety of shooting situations (position, lighting, hairdo, emotion, backdrop, etc.) and face traits requires versatility. Deep learning-based image identification methods beat machine learning methods in efficiency and information processing. Modern computer systems have major authentication issues. Internet-connected smart devices are producing more data every day. A new model is needed to handle its vast data output. Deep learning and edge computing process vast volumes of data with high precision. Many trust facial recognition systems. SIFT and accelerated robust features are used in traditional facial recognition algorithms (SURF). This paper presents a convolutional neural network-based face identification and recognition solution that outperforms established methods. Tagged photographs of people taken in the outdoors teach the face-recognition algorithm (LFW). The suggested system had 99.1% accuracy on test data. © 2023 IEEE.

关键词： Face recognition

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ECO-Guard: An Integrated AI Sensor System for Monitoring Wildlife and Sustainable Forest Management 8th

ECO-Guard: An Integrated AI Sensor System for Monitoring Wil...

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8th International conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Nikhilesh Krishna, Ch. Rauniyar, Avishek Kireeti Sai Bharadwaj, N. Raj, Sujay Bharath valsan, vipina Suresh, Kavya Ravikumar Pandi, v. Sathyan, Soumya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819994885

In India, 12% of wild animals are threatened with extinction, and human activity threatens more than 80% of East and Southeast Asia's wetlands. Every year, an estimated 400 people are killed by wildlife in India, with Maharashtra alone seeing 88 human deaths and 9258 cattle killings by tigers in 2020. The ECO-Guard is an intelligent device which uses the You Only Look Once (YOLO) model and various sensors and machine learning models with algorithms that support and promote forest monitoring and tourist guidance in forest areas addressing Sustainable Development Goal (SDG) 12 (Life on Land). The paper presents a linear chain-like mesh of devices speeded across bio-area which are used in correlation for intended surveillance. Each device can multimodule apps and tally their sightings automatically to analyze coherence of the situation with the forest rangers, organizations, and figures. It catches from the information, the ill substitutes like human spotting with beasts. It suggests that the advice can be further put into practice by constantly monitoring any closeness of alternatives that match to create an unfavorable combination. Also having to help detect many forest crimes, some including illegal logging, preventing, and deforestation on a large scale. This paper highlights a novel ECO-Guard, an intelligent and autonomous monitoring system which includes artificial intelligence (AI)-enhanced strategies and algorithms to conserve wildlife and forest ecosystems by integrating various hardware and software components. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deforestation

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FPGA-Based Dynamic Deep Learning Acceleration for Real-Time video Analytics 35th

FPGA-Based Dynamic Deep Learning Acceleration for Real-Time ...

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35th International conference on Computer Architecture (ARCS)

作者： Lu, Yufan Gao, Cong Saha, Rappy Saha, Sangeet McDonald-Maier, Klaus D. Zhai, Xiaojun Univ Essex Colchester Essex England

ISBN: (纸本)9783031218668;9783031218675

Deep neural networks (DNNs) are a key technique in modern artificial intelligence that has provided state-of-the-art accuracy on many applications, and they have received significant interest. The requirements for ubiquity of smart devices and autonomous robot systems are placing heavy demands on DNNs-inference hardware, with high requirement for energy and computing efficiencies, along with the rapid development of AI techniques. The high energy efficiency, computing capabilities, and reconfigurability of FPGAs make these a promising platform for hardware acceleration of such computing tasks. This paper primarily addresses this challenge and proposes a new flexible hardware accelerator framework to enable adaptive support for various DL algorithms on an FPGA-based edge computing platform. This framework allows run-time reconfiguration to increase power and computing efficiency of both DNN model/software and hardware, to meet the requirements of dedicated application specifications and operating environments. The achieved results show that with the proposed framework is capable to reduce energy consumption and processing time up to 53.8% and 36.5% respectively by switching to a smaller model. In addition, the time and energy consumption are further elaborated with a benchmark test set, which shows that how input data in each frame and size of a model can affect the performance of the system.

关键词： Deep neural networks Hardware accelerator Edge computing Real-time video analytics FPGAs

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Parametric Synthesis of Quantum Circuits for Training Perceptron Neural Networks

Parametric Synthesis of Quantum Circuits for Training Percep...

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2022 Intelligent Technologies and Electronic devices in vehicle and Road Transport Complex, TIRvED 2022

作者： Ostroukh, A.v. Pronin, C.B. volosova, A.v. Subbotin, B.S. Smirnov, P.I. Moscow Automobile and Road Construction State Moscow Russia

ISBN: (数字)9798350397703

ISBN: (纸本)9798350397703

This work contains the analysis of results received after running synthesized quantum circuits for training perceptron neural networks. The training is performed by creating a Grover's algorithm with a custom oracle function. The concept of synthesizing quantum circuits was showcased in the process of generating training circuits for three perceptron topologies, which were designed to test the accuracy of the synthesis process. The test circuits serve to prove that the proposed synthesis approach could be scaled to utilize more complex quantum computing systems and to solve more practical tasks. IBM's 100-qubit cloud quantum simulator was used as the debugging environment. Quantum circuits for described algorithms are generated by the "Naginata"quantum synthesizer, its source code is published and further documented on GitHub along with the code for the provided example algorithms. The article describes the processes behind the algorithm for synthesizing quantum circuits that perform the training process of single-layer perceptrons by finding their weights by filtering all possible input values through a predefined accuracy criterion. Since quantum computing is still in its early development phase, quantum circuits are created mainly by manual placement of logic elements. Implementing quantum algorithms, especially more use-case specific ones, directly on the quantum circuit level could lead to the circuit easily becoming too complex for human comprehension. Quantum Circuit Synthesizer "Naginata"was created to simplify the development and debugging process of quantum algorithms, by adding better clarity to their development process. In our case, better clarity for the development process is achieved by composing functions for commonly used operations performed in the implemented quantum algorithm. The programmer could now implement the quantum algorithm as a set of functions, instead of manually creating a circuit from single logic elements. After this, the synthesi

关键词： Machine learning

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