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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Rengaraj, R. Venkatakrishnan, G.R. Rajalakshmi, M. Mahalakshmi, K. Arini, R.S.M. Sri Sivasubramaniya Nadar College of Engineering Department of Electrical and Electronics Engineering Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331508845

India is the world's third largest producer and third largest consumer of electricity. Traditional billing systems in India lack real-time monitoring, making it difficult to address peak demand efficiently. They often lead to inaccurate billing due to manual readings. These systems do not incentivize offpeak consumption, hindering efforts to optimize energy usage. Embracing modern approaches like IoT and Time-of-Use pricing can enhance accuracy, promote energy efficiency, and also optimize energy consumption based on the demand requirement. To address the increasing energy demands and environmental concerns, the integration of Internet of Things (IoT) technologies, Time-of-Use (TOU) pricing strategies, and machine learning algorithms presents a transformative approach to revolutionize electric billing management. Developing a comprehensive system that combines real-time data acquisition through IoT devices with dynamic pricing based on TOU can optimize energy consumption patterns. Machine learning models are employed to analyze historical usage data, predict consumption patterns, and dynamically adjust pricing to incentivize efficient energy utilization. This aims to enhance user awareness, encourage responsible energy usage, and contribute to a sustainable and resilient energy ecosystem. © 2024 IEEE.

关键词： billing management efficient energy utilization Internet of Things machine learning Time of use (TOU)

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Enhancing Healthcare AI Sensitivity through Affective computing: Analyzing Human Emotions 1

Enhancing Healthcare AI Sensitivity through Affective Comput...

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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Singh, Rahul Kumar, Avadhesh Sagar, Shraddha Sharma, Vishnu Galgotias University School of Computing Science and Engineering Greater Noida India ITS Engineering College Department of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350356816

Affective computing (AC), also known as emotion AI, is a concept that applies modern technology to help systems to recognize and process human emotions and their feelings. Several uses of affective computing have been demonstrated, including marketing, healthcare, driving assistance, and education. Linear Support vector machines (SVM), k-nearest neighbor (k-NN), Hidden Markov models (HMMs), Gaussian mixture models (GMM), discriminate classifiers (LDC), decision tree algorithms and artificial neural networks (ANN), are the classifiers that are most frequently used in research on identifying and categorizing human emotions. Selecting the right classifier can significantly increase the system's overall efficiency, as numerous studies have conclusively demonstrated. Through use of cameras, microphones, skin sensors, or other devices, affective computing gathers signals from human users, gathering data on their facial expressions, voice tones, gestures, and other characteristics that can hint at their emotional states. The system deduces the user's emotional state from those data points. This study emphasizes on the fundamentals of computing emotions. © 2024 IEEE.

关键词： Support vector machines

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Anomalies in light scattering: A circuit-model approach

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Physical Review Applied 2024年第3期22卷 034061页

作者： Deepanshu Trivedi Arjuna Madanayake Alex Krasnok Department of Electrical and Computer Engineering Knight Foundation School of Computing and Information Sciences

Photonics is crucial for communication and computing, as it involves controlling how light scatters. Recent breakthroughs like bound states in the continuum (BICs) and parity-time-symmetric systems are linked to the scattering matrix, governed by passivity and causality. However, using complex-frequency excitations overcomes these limitations, allowing us to study systems with losses or gains and discover phenomena like virtual critical coupling and virtual gain. Applying complex-frequency-excitation concepts to more complicated systems is challenging. In this work, we extend the lumped-element model from circuit theory to analyze light-scattering anomalies in the complex-frequency domain. This approach helps us design and understand effects such as virtual perfect absorption, BICs, real and virtual critical coupling, exceptional points, and anisotropic transmission resonances. These insights broaden our knowledge of electromagnetic wave phenomena and open possibilities in photonics, offering innovative ways to create and optimize optical devices and systems for various applications.

关键词： Classical optics Light propagation, transmission & absorption Light-matter interaction Photonics Scattering theory Wave scattering Non-Hermitian systems Light scattering

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Robust Authentication: Leveraging Hardware Fingerprints and AI to Enhance Security Against Spoofing

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IEEE Access 2025年 13卷 86912-86926页

作者： Nimmy, K. Jain, Kurunandan Sachin, S.M. Abekaesh, P.A. Venkitasubramaniam, Parv Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri690525 India Center for Cybersecurity Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri Campus Kerala Amritapuri690525 India Lehigh University Department of Electrical and Computer Engineering BethlehemPA18015 United States

Fingerprint authentication blends biometric precision with user convenience, leveraging unique ridge patterns for fast, reliable, and tamper-resistant identity verification. However, it remains vulnerable to spoofing attacks, where adversaries create synthetic fingerprints using materials like gelatin. To mitigate this, multi-factor authentication (MFA) enhances security by verifying both the fingerprint and the user’s device, restricting access to authorized hardware and complicating attacks. Although Photo Response Non-Uniformity (PRNU) has been explored for device authentication, its reliability diminishes under varying lighting conditions. To address this, we propose an AI-driven approach to generate robust hardware fingerprints from fingerprint scanners, enabling high-accuracy device identification. Our method, trained on 2,000 fingerprint images from 10 scanners, achieves an impressive 98% accuracy in identifying the source scanner. By integrating fingerprint biometrics, hardware fingerprints, and passwords, our novel MFA protocol significantly enhances security against spoofing and forgery attacks. Through security analysis, we demonstrate its effectiveness in preventing unauthorized access. Finally, we validate our authentication framework using an AI-powered binary classifier model, showcasing its practicality for real-world security applications. © 2013 IEEE.

关键词： Authentication Protocol

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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

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computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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The Smart Application Development in Real Time Parallel Applications with Industrial Automation 2

The Smart Application Development in Real Time Parallel Appl...

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2nd IEEE International Conference on Distributed computing and electrical Circuits and Electronics, ICDCECE 2023

作者： Khan, Gulista Zabeeulla, Zabeeulla Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Real-time parallel applications with industrial automation are applications that are designed to run in parallel, in order to accomplish a task in a shorter amount of time. These applications are used in industrial environments to control machines and other processes in order to maximize efficiency and minimize waste. Real-time parallel applications allow machines and processes to be managed simultaneously. This means that instead of needing to wait for one process to finish before starting the next, multiple processes can be run at the same time. This increases the speed at which tasks can be completed, allowing for greater efficiency and productivity. Real-time parallel applications can be used to automate processes and machines. This means that instead of needing to manually manage and monitor a machine, the software can handle all of the work. This can help to reduce errors and improve the overall quality of the product being produced. © 2023 IEEE.

关键词： Efficiency

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An Smart Analysis of Energy Efficiency Issues in Industrial Automation 2

An Smart Analysis of Energy Efficiency Issues in Industrial ...

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2nd IEEE International Conference on Distributed computing and electrical Circuits and Electronics, ICDCECE 2023

作者： Singhal, Priyank Mahesh, T.R. Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Industrial automation has become increasingly important in recent years. It has allowed for increased efficiency, productivity and safety in the workplace, as well as improved quality control of products. However, with the increased use of automation, comes the need to be mindful of energy efficiency issues. Energy use in industrial automation is primarily driven by the use of electric motors, pumps and other devices. These devices consume a large amount of electricity, especially when they are running continuously or at high speeds. This can result in significant energy losses and increased costs. Additionally, when the motors are operating at different speeds, they can draw more power than they need and this can also cause energy losses. To reduce energy losses, it is important to ensure that the motors and pumps are properly sized for the application and that they are running at the correct speed. Additionally, it is important to take into account the efficiency of the motors and pumps. For example, variable speed drives can introduce energy savings in certain applications. Additionally, energy-efficient motors and pumps can reduce energy losses significantly. © 2023 IEEE.

关键词： Automation

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A Novel Attention-Based Method for Integrating Facial and Speech Features in Emotion Recognition 2

A Novel Attention-Based Method for Integrating Facial and Sp...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Singh, Rahul Sagar, Shraddha Kumar, Avadhesh Sharma, Vishnu Galgotias University School of Computing Science & Engineering Greater Noida India Its Engineering College Department of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350388916

Our research introduces a novel approach for recognizing multiple emotions by leveraging perspective-based methods. This technique integrates facial features and speech characteristics obtained from different encoders to pinpoint the most significant attributes. The distinct characteristics of speech and facial expressions, along with effective size management, prove to be highly beneficial. By concentrating on specific components, the system enhances overall accuracy. Images of facial features are captured from both low and high angles to ensure a comprehensive representation. These techniques are merged through a fusion mesh, producing several feature vectors that are subsequently utilized in the classification stage for sensitivity analysis. When tested with both data sets, this algorithm surpassed existing models, achieving a weighted accuracy (WA) of 90.3%. © 2024 IEEE.

关键词： Attention-Based Method Emotion Classification Facial and Speech Features Fusion Network Multimodal Emotion Recognition Weighted Accuracy

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Defect Prediction Using Akaike and Bayesian Information Criterion

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computer Systems Science & engineering 2022年第6期41卷 1117-1127页

作者： Saleh Albahli Ghulam Nabi Ahmad Hassan Yar Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Electrical and Computer Engineering Air UniversityIslamabadPakistan

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the *** of the work present in software defect prediction is focused on the selection of best prediction *** this purpose,deep learning and ensemble models have shown promising *** contrast,there are very few researches that deals with cleaning the training data and selection of best parameter values from the *** data available for training the models have high variability and this variability may cause a decrease in model *** deal with this problem we used the Akaike information criterion(AIC)and the Bayesian information criterion(BIC)for selection of the best variables to train the model.A simple ANN model with one input,one output and two hidden layers was used for the training instead of a very deep and complex *** and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best *** first,variables were narrowed down to a smaller number using correlation *** subsets for all the possible variable combinations were *** the end,an artificial neural network(ANN)model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC *** was found that combination of only two variables’ns and entropy are best for software defect prediction as it gives minimum AIC and BIC ***,nm and npt is the worst combination and gives maximum AIC and BIC values.

关键词： Software defect prediction machine learning AIC BIC model selection cross-project defect prediction

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Maximizing Profit of the Service Providers through Efficient Content Placement and Retrieval in an IoT-enabled Edge-Cloud Platform 18

Maximizing Profit of the Service Providers through Efficient...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Gandhi, Preet Mahanta, Abhishriya Ghose, Manojit Sahu, Aryabartta Indian Institute of Technology Department of Computer Science and Engineering Guwahati India Indian Institute of Technology School of Computing and Electrical Engineering Mandi India Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350391725

In today's 5G era, the amount of data generated by the Internet of Things (IoT) devices is enormous. Data is processed and stored in the cloud under a traditional cloud computing architecture, and real-time processing and retrieval of these data is time-consuming. Therefore, an edge-cloud framework comes in handy where the content generated is placed on the edge nodes of the network, and it can reach the endusers in a time-bound manner. A majority of the previous research works on content placement under the edge-cloud architecture primarily aimed at improving the latency of the system. However, the approach proposed in this paper aims to maximize the profit of the service provider by effectively placing the content in a hierarchically organized edge-cloud network. We propose two strategies and compare them with random and greedy approaches for both synthetic and real data sets. The experimental results demonstrate the effectiveness of the proposed strategies, and the profit of the service providers increases by 38.81% (maximum) and 10.51% (on average) for the synthetic dataset and by 65.74% (maximum) and 22.83% (on average) for the real-world dataset when compared with the greedy approach. © 2024 IEEE.

关键词： Cloud platforms

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