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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： T Lakshmi Narayanaa R R Skandarsini S. Jhansi Ida S. Rathana Sabapathy P Nanthitha Dept. of Computer Science and Business Systems RMK Engineering College Thiruvallur India Dept. of Information Technology Panimalar Engineering College Chennai India

This research study focuses on the prediction of inflation rates using two distinct models, Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM), tailored to accommodate the temporal resolutions of monthly and quarterly inflation data. While a comprehensive evaluation across varying time horizons was not conducted, the results suggest that the ARIMA model excels in short-term forecasting, as evidenced by its impressive performance on a 2021 holdout dataset. Conversely, the LSTM model displays potential for medium-term predictions. This study underscores the significance of aligning forecasting models with data characteristics, emphasizes the importance of temporal resolution, and discusses potential avenues for improvement, such as multivariate modeling or alternative techniques like Prophet. The findings have broad implications for economists, policymakers, and analysts seeking precise inflation forecasts, shedding light on the nuanced interplay between models and data in the domain of inflation prediction.

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Machine Learning Approaches To Predict The Stability of Smart Grid

Research Square

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Research Square 2024年

作者： Satu, Shahriare Khan, Imran Dept. of Management Information Systems Noakhali Science and Technology University Noakhali3814 Bangladesh Dept. of Computer Science and Engineering Gono Bishwabidyalay Dhaka1344 Bangladesh

The smart grid is a modern solution to generate, distribute, and use energy effectively and efficiently. Ensuring the stability of the smart grid is critical to guarantee safe and consistent operation. This study proposes a machine learning model to identify smart grid stability more efficiently. To accomplish this, we collected the electrical grid stability dataset from a public machine learning repository and analyzed the correlation of individual features. Different feature selection and transformation techniques were employed to generate subsequent feature sets. Then, various classification techniques were applied to the primary and its subsets to identify the best machine-learning model for predicting smart grid stability more efficiently. The results indicated that MLP showed the highest accuracy (97.06%) for the Z-score transformed data set using Grid Search Optimization. This study demonstrates the potential of using machine learning to predict smart grid stability accurately and efficiently, providing a critical tool for improving the reliability, efficiency, and sustainability of the energy industry. © 2024, CC BY.

关键词： Smart power grids

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Prospects of Solar Cell Technology from Silicon to Carbon Nanotube 9

Prospects of Solar Cell Technology from Silicon to Carbon Na...

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9th IEEE International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, SETIT 2022

作者： Shabir, Aamar Cao, Jing Fouad, Emadelden Ullah, Muhammad Florida Polytechnic University Dept. of Electrical and Computer Engineering Lakeland United States C. Leon King High School IB Program Tampa United States Florida Polytechnic University Dept. of Engineering Physics Lakeland United States

ISBN: (数字)9781728184425

ISBN: (纸本)9781728184425

Solar cell is becoming one of the vibrant power generations among all the renewable energy resources and demanding the most attention of researchers to achieve the goal of increasing stability and efficiencies. To reinforce the advancement of the solar cell, carbon nanotubes (CNTs) have come out as a highly potential candidate due to the reasons of chirality, mean free path, conductivity, current density, etc. The CNT has a unique size, structure, and attractive characteristics which enable the bold concept of getting maximum power from a solar cell. The recent availability of the CNT in quantities and formats amenable opens a new window to improve the efficiency of the solar cell. This paper will present a prospect of the recent research about CNTs and their contribution as a potential material in solar cell technology. © 2022 IEEE.

关键词： Carbon nanotubes

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LoRaWAN Scheduling Mechanism for 6G-Based LEO Satellite Communications

LoRaWAN Scheduling Mechanism for 6G-Based LEO Satellite Comm...

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IEEE Conference on Vehicular Technology (VTC)

作者： Abhijeet Manoj Varma Nikumani Choudhury Jay Dave Anakhi Hazarika Moustafa M. Nasralla Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Pilani Hyderabad India Dept. of Electrical & Electronics Engineering Birla Institute of Technology & Science Pilani Hyderabad India Dept. of Communications & Networks Engineering Smart Systems Engineering Lab Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

As the Internet of Things (IoT) is poised to become a global phenomenon, it is imperative to schedule the transmissions of IoT devices effectively and in a fair way. Leveraging Long Range (LoRa) technology, we can achieve transmissions that consume minimal power while covering vast distances, aligning with the requirements of IoT devices. However, the proximity of multiple devices within the same area often leads to packet interference and collisions. To address this, our study introduces a pioneering scheduling method utilizing a constellation of Low Earth Orbit (LEO) satellites to manage and streamline the transmission of data from End Devices (EDs). This method employs two LEO satellites: the first satellite assigns the sequence for EDs to dispatch their packets, and the second collects these packets in the predetermined sequence before forwarding them to the LoRa Network Server (LNS). For urgent (URG) communications, EDs can alert the first satellite, which then coordinates with the LNS to schedule these priority transmissions. The LNS generates a schedule that is relayed to the second satellite, informing EDs with URG packets of their specific transmission times and channels. This scheduling approach is designed to optimize channel usage effectively while accommodating the transmission of urgent data.

关键词： 6G mobile communication Schedules Vehicular and wireless technologies Satellites Scheduling algorithms Low earth orbit satellites LoRaWAN

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Automatic Network Intrusion Detection System Using Machine Learning and Deep Learning

Automatic Network Intrusion Detection System Using Machine L...

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Artificial Intelligence and Mechatronics systems (AIMS), International Conference on

作者： Mohammed Mynuddin Sultan Uddin Khan Zayed Uddin Chowdhury Foredul Islam Md Jahidul Islam Mohammad Iqbal Hossain Dewan Mohammed Abdul Ahad Dept. of Electrical Engineering North Carolina A & T State University Data Scientist Data Bid Machine Inc. Associate Power Systems Engineer-II Open Systems International Inc. Dept. of Electrical Engineering Tuskegee University Dept. of Electrical and Computer Engineering University of North Carolina at Charlotte

In recent years, the popularity of network intrusion detection systems (NIDS) has surged, driven by the widespread adoption of cloud technologies. Given the escalating network traffic and the continuous evolution of cyber threats, the need for a highly efficient NIDS has become paramount for ensuring robust network security. Typically, intrusion detection systems utilize either a pattern-matching system or leverage machine learning for anomaly detection. While pattern-matching approaches tend to suffer from a high false positive rate (FPR), machine learning-based systems, such as SVM and KNN, predict potential attacks by recognizing distinct features. However, these models often operate on a limited set of features, resulting in lower accuracy and higher FPR. In our research, we introduced a deep learning model that harnesses the strengths of a Convolutional Neural Network (CNN) combined with a Bi-directional LSTM (Bi-LSTM) to learn spatial and temporal data features. The model, evaluated using the NSL-KDD dataset, exhibited a high detection rate with a minimal false positive rate. To enhance accuracy, K-fold cross-validation was employed in training the model. This paper showcases the effectiveness of the CNN with Bi-LSTM algorithm in achieving superior performance across metrics like accuracy, F1-score, precision, and recall. The binary classification model trained on the NSL-KDD dataset demonstrates outstanding performance, achieving a high accuracy of 99.5% after 10-fold cross-validation, with an average accuracy of 99.3%. The model exhibits remarkable detection rates (0.994) and a low false positive rate (0.13). In the multiclass setting, the model maintains exceptional precision (99.25%), reaching a peak accuracy of 99.59% for $\text{k-value} =10$ . Notably, the Detection Rate for $\text{k-value}=10$ is 99.43%, and the mean False Positive Rate is calculated as 0.214925.

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Mutual authentication method between PUFs

Mutual authentication method between PUFs

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2022 IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Choi, Yunyeong Lee, Jungwon Shin, Hyungsoon Sun, Wookyung Ewha Womans University Dept. of Electronic and Electrical Engineering Seoul Korea Republic of Seoul National University Dept. of Electrical and Computer Engineering Seoul Korea Republic of Ewha Womans University Smart Factory Multidisciplinary Program Seoul Korea Republic of

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

This paper proposes a new authentication method that enables mutual verification between two different PUFs without a server. The output signal (response) of one PUF is hidden in the opponent's input signal (challenge) list by interlinking the challenge and response information of the two PUFs. In this way, when sending and receiving authentication information, a challenge list index is used instead of exposing the challenge or response information to the network directly. The verification information for checking the validity of the received index is protected with encryption using a hash function. Also, two novel PUF systems, key-map PUF and cube PUF, are presented that improve the security of the PUF-based system using previous authentication information. The proposed authentication method and PUF systems can be used for IoT devices. © 2022 IEEE.

关键词： Hash functions

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Trusted Access to 6G Testbeds Through a Security Intent-Driven Software-Defined Perimeter Framework 29

Trusted Access to 6G Testbeds Through a Security Intent-Driv...

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Fernandez-Martinez, Carolina Bikos, Anastasios Verikoukis, Christos Siddiqui, Shuaib i2CAT Foundation Barcelona08034 Spain Universitat Pompeu Fabra Dept. of Information and Communication Technologies Barcelona08018 Spain Iquadrat Informatica Sl Barcelona08006 Spain Universitat Politècnica de Catalunya Dept. of Signal Theory and Communications Barcelona08034 Spain Industrial Systems Institute Athena Research Center Patras26504 Greece University of Patras Dept. of Computer Engineering and Informatics Patras26504 Greece

ISBN: (纸本)9798350377644

Parallel to the evolution of recent trends in the cybersecurity industry and the increase of cyberattacks in the last few years, there is renovated interest on the application of software-defined techniques to enforce best security practices, such as in the Software-Defined Perimeter specification and the Zero Trust architectures. This work proposes a software-defined network segmentation and access control framework aligned with these principles. This solution captures minimal security requirements from testbed operators to establish ad-hoc, isolated connectivity and access control for end-users (experimenters) interacting with exposed endpoints from the exposed services in the cloud testbeds. The elicited architecture, design decision and initial implementation are introduced. © 2024 IEEE.

关键词： Testbeds

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Data Security Enhancement in Cloud Computing by Proposing A DKE Encryption Protocol 23

Data Security Enhancement in Cloud Computing by Proposing A ...

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Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications

作者： Sahar Ebadinezhad Halmat Ayub Abdulmajed Department of Computer Information Systems Near East University North Cyprus and Computer Information Systems Research and Technology Center (CISRT) Near East University North Cyprus Dept. Engineering and Computer Systems Lebanese French University (LFU) Iraq

ISBN: (纸本)9798400708268

This study uses cryptography to tackle the important problem of data security in cloud computing. Two keys are used in the Dual Key Encryption (DKE) method for both encryption and decryption. DKE employs a public key for the first encryption round before cloud upload and a user-only private key for the second round of encryption. Decryption operates oppositely. When tested and simulated with different file sizes on a CloudAnalyst simulator, DKE operates faster and more efficiently than cryptographic methods such as Triple DES (3DES), AES, RSA, and DES. Thanks to developments in information technology, cloud computing has made several services available online. Data security assurance is still very difficult to achieve, nevertheless. In this context, cryptography is essential and has led to the creation of the novel DKE technology. This technology is not only far more efficient than well-known cryptographic techniques such as 3DES, but it also improves data security.

关键词： Cloud Services Cryptography Data Integrity Data Security Dual Key Encryption

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CONV1D-GRU: A Hybrid Model for Short-Term Electrical Load Forecasting

CONV1D-GRU: A Hybrid Model for Short-Term Electrical Load Fo...

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Telecommunications Conference (ITC-Egypt), International

作者： Karim S. Farid A.A. Ali Sameh A. Salem Amr E. Mohamed dept. Information Systems & Communications Egyptian Electricity Holding Company Cairo Egypt dept. Electrical Power Helwan University - Faculty of Engineering Cairo Egypt dept. Computer & Systems Engineering Helwan University - Faculty of Engineering Cairo Egypt

With the development of plans to replace conventional meters with smart meters to enhance the functionality and performance of power system, forecasting power consumption is still a challenge due to nonlinearity patterns, different types of noise in the datasets, and sometimes lack of data. Artificial Intelligence plays an important role in utilizing the data sent from smart meters, particularly in the area of short-term load forecasting, which is required to meet the needs of consumers. Based on such insight, this study suggests a hybrid model based on a one-dimension convolutional neural network and gated recurrent units (CONV1D-GRU), to forecast hourly power consumption. The outcome demonstrates that the suggested model is capable of encompassing the patterns in a historical data pertaining the hourly power use. The suggested model has outperformed the competing strategies in the mean absolute percentage error (MAPE%), and the coefficient of determination $(R^{2})$ metrics.

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TCN-MLP: A Hybrid Model for Short-Term Electrical Load Forecasting

TCN-MLP: A Hybrid Model for Short-Term Electrical Load Forec...

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Intelligent Methods, systems, and Applications (IMSA)

作者： Karim S. Farid A. A. Ali Sameh A. Salem Amr E. Mohamed Dept. Information Systems & Communications Egyptian Electricity Holding Company Cairo Egypt Dept. Electrical Power Helwan University - Faculty of Engineering Cairo Egypt Dept. Computer & Systems Engineering Helwan University - Faculty of Engineering Cairo Egypt

Using artificial intelligence (AI) to anticipate electrical consumption is one of the used applications of this technology. The analysis and training of AI models are done using data related to electricity consumption, including consumption itself, temperatures, working hours, the number of inhabitants, etc. AI models can help to reduce electricity expenses and consumption, which helps in identifying peak use periods and creating more efficient energy plans. Another use of artificial intelligence is to advise users on how to use less energy in a more efficient manner. Based on such insight, this study suggests a hybrid model based on a temporal convolutional network and multilayer perceptron (TCN-MLP), to forecast hourly power consumption. The results show that the recommended model can account for the patterns in the historical data relating to hourly power usage. TCN-MLP hybrid model has proved to be better than competing approaches in the coefficient of determination (R 2 ) and the mean absolute percentage error (MAPE%) metrics.

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