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8th IEEE International Conference on Energy Internet, ICEI 2024

作者： Mehta, Yukta Lo, Vincent Mehta, Vijen Agarwal, Kunal Madabathula, Charan Teja Gao, Jerry San Jose State University Department of Applied Data Science San JoseCA United States Evergreen Valley High School Department of Analysis San JoseCA United States San Jose State University Department of Computer Engineering San JoseCA United States

ISBN: (纸本)9798331523558

According to the U.S. Energy Information Administration, 60 percent of the world's electricity is generated from fossil fuels, 18 percent from nuclear power, and only 21 percent from green energy resources. These statistics highlight the pressing need to transition to renewable energy for a sustainable future. However, a major challenge that arises from this increased demand for green energy is the risk of electricity shortage. Overcoming this challenge requires a shift from conventional energy management to an AI-based electricity management system that can more efficiently balance supply and demand. In this paper, we have developed a comprehensive cloud-based Green AI service Management System to accommodate three important aspects of green energy services: electricity consumption, electricity generation, and shortage analysis. In this system, we propose using a community-based model that manages and analyzes multiple buildings' energy usage, allowing the model to be trained further via distributed and aggregated training. The models developed for these components use three approaches: deep learning, machine learning, and reinforcement algorithms, and achieved a shortage analysis accuracy of 98.2 percent. The model is deployed on the cloud and results are accessible from the user interface. Overall, we provide a unique Green AI solution to the market using a system that can handle various aspects of green energy and gives better accuracy than existing systems. © 2024 IEEE.

关键词： Consumption prediction Electricity Management System Electricity shortage forecasting Green AI Usage prediction

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Paddy Plant Disease Recognition, Risk Analysis, and Classification Using Deep Convolution Neuro-Fuzzy Network

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Journal of Mobile Multimedia 2022年第2期18卷 325-348页

作者： Vinoth Kumar, V. Karthick Raghunath, K.M. Rajesh, N. Venkatesan, Muthukumaran Joseph, Rose Bindu Thillaiarasu, N. School of Computer Science and Engineering Jain (Deemed to be University) Bangalore India Department of Computer Science and Engineering MVJ College of Engineering Bangalore India University of Technology and Applied Science Shinas Oman Department of mathematics School of Applied Sciences REVA University Karnataka Bengaluru India Department of Mathematics Christ Academy Institute for Advanced Studies Bangalore India School of Computing and Information Technology REVA University Bengaluru India

A significant number of the world’s population is dependent on rice for survival. In addition to sugarcane and corn, rice is said to be the third most growing staple food in the world. As a consequence of intensive usage of man-made fertilizers, paddy plant diseases have also risen at a faster pace in current history. Exploring the possible disease spread and classifying to detect the consequent impact at an early stage will prevent the loss and improve rice production. The core task of this research is to recognize and quantify different kinds of infections (disease) affecting the paddy plant crop, such as brown spots, bacterial blight, and leaf blasts. Both detection and recognition are carried out based on the risk analysis of paddy crop leaf images. We suggest a Deep Convolutional Neuro-Fuzzy Method (DCNFM) that combines one of the advanced machine learning variant, namely deep convolutional neural networks (DCNNs) and uncertainty handler called fuzzy logic. The synthesis has the benefits of both fuzzy logic and DCNNs when dealing with unstructured data, extracting essential features from imprecise and ambiguous datasets. From the crop field, continuous image data are captured through image sensors and fed as a primary input to the proposed model to analyze the risk and then later to classify them for precise recognition/detection of the disease. The detection/recognition rate of the DCNFM is found to be 98.17% which is comparatively found to be effective in comparison with the traditional CNN model. © 2021 River Publishers

关键词： Accuracy Blast Bronzing Convolution neuro-fuzzy Crown rot Disease classification Head blight Paddy

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Employment Prediction System Based on Recommender System Using NLP, Content Filtering & Collaborative Filtering 10

Employment Prediction System Based on Recommender System Usi...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Gupta, Sanjai Kakde, Vinodkumar Dharamshi, Ravindra R. Sakarkar, Gopal Abiyasa, Agus Putu University of Technology and Applied Sciences Nizwa Oman University of Nizwa Nizwa Oman Dr. Vishwanath Karad MIT World Peace University Department of Computer Science and Applications Pune India Universitas Pendidikan Nasional Department of Electrical Engineering Denpasar Indonesia

ISBN: (纸本)9798350363104

Employability is an important measure for Higher education Institutions/universities to measure success. Due to globalization, automation, and advances in Data analytics, the job market landscape is more dynamic than ever. All the stakeholders like employers, graduates, and Academic Institutions are extremely beneficial if they are aware of the factors affecting employability and skills requirements. For better career opportunities, students may need to know their strengths and weaknesses. Academic institutions, Universities, and others involved in designing curricula can develop more appropriate skill sets required for the job market. Recommender systems provide suggestions for items that a user may find useful. Using data obtained from job portals and social networking sites such as LinkedIn, in this research project we aim to develop a job recommendation system. Data collection and Analysis were done through web mining to acquire job portal data, social networking data for CVs/resumes, and curriculum data of various academic institutions in Oman. To extract meaningful information from this data, we use Natural Language Processing (NLP) techniques. To match job seekers with suitable job opportunities, our recommendation system utilizes cosine similarity based on content-based filtering. In this study, the effectiveness of the We also create a user-friendly interface that displays essential details such as skills needed, job roles, and company information based on the user's skills and resumes. The proposed model will be evaluated in terms of its ability to provide accurate recommendations. Users will benefit from an efficient and personalized job search process through this system. © 2024 IEEE.

关键词： Collaborative filtering

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Inventory Model for Imperfect Deteriorating Items Under Fuzzy Environment 11

Inventory Model for Imperfect Deteriorating Items Under Fuzz...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Mittal, Mandeep Pandey, Jayanti Tripathi Jain, Muskan School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Amity Institute of Applied Sciences Amity University Uttar Pradesh Department of Mathematics Uttar Pradesh Noida India

ISBN: (纸本)9798350350357

The aim of this study is to introduce the application of fuzzy numbers in an Economic Order Quantity (EOQ) model for defective items under deterioration. In practice, the fuzziness of parameters tends to reduce over time compared to the theoretical assumptions, which justifies the use of fuzzy numbers. The proposed model starts with crisp approach which later involves the fuzzification of inventory parameters, such as demand rate, holding cost, deterioration rate, purchase cost, screening cost, and ordering cost, to account for uncertainty and imprecision. The triangular fuzzy numbers are utilized to represent these parameters, and a fuzzy model is developed accordingly. To defuzzify the model, Yager's ranking index is adopted, and a detailed comparison is made between crisp and general fuzzy solutions. The model is validated through numerical examples and graphical illustrations, followed by a thorough sensitivity analysis to gain a deeper comprehension of how modifications in parameters affect the optimal solution. © 2024 IEEE.

关键词： Deterioration

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Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm

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Intelligent Automation & Soft Computing 2023年第9期37卷 3159-3177页

作者： Nitin Mittal Rohit Salgotra Abhishek Sharma Sandeep Kaur SSAskar Mohamed Abouhawwash University Center for Research and Development Chandigarh UniversityMohaliPunjab140413India Faculty of Physics and Applied Computer Science AGH University of Science&TechnologyKrakowPoland Faculty of Information Technology Middle East UniversityAmman11813Jordan Department of Computer Engineering and Applications GLA UniversityMathura281406India Department of Computer Engineering&Technology Guru Nanak Dev UniversityAmritsarPunjab143005India Department of Statistics and Operations Research College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia Department of Computational Mathematics Science and EngineeringCollege of EngineeringMichigan State UniversityEast LansingMI48824USA Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt

The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this *** Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of *** has already proved its competence in various optimization prob-lems,but it suffers from slow convergence *** improve the convergence performance of FA,a new variant named EFA is *** effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and *** an application of this algorithm to real-world problems,EFA is also applied to optimize the CR *** is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity ***,it requires optimization to meet specific performance *** results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic *** results further prove that the proposed algorithm is highly efficient and provides superior results.

关键词： Firefly algorithm cognitive radio bit error rate genetic algorithm simulated annealing biogeography-based optimization

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Directional emission of a readout resonator for qubit measurement

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Physical Review applied 2024年第3期22卷 034035页

作者： Alec Yen Yufeng Ye Kaidong Peng Jennifer Wang Gregory Cunningham Michael Gingras Bethany M. Niedzielski Hannah Stickler Kyle Serniak Mollie E. Schwartz Kevin P. O’Brien Department of Electrical Engineering and Computer Science Research Laboratory of Electronics Harvard John A. Paulson School of Engineering and Applied Sciences Lexington Massachusetts 02421 USA

We propose and demonstrate transmission-based dispersive readout of a superconducting qubit using an all-pass resonator, which preferentially emits readout photons toward the output. This is in contrast to typical readout schemes, which intentionally mismatch the feedline at one end so that the readout signal preferentially decays toward the output. We show that this intentional mismatch creates scaling challenges, including larger spread of effective resonator linewidths due to nonideal impedance environments and added infrastructure for impedance matching. A future architecture using multiplexed all-pass readout resonators would avoid the need for intentional mismatch and potentially improve the scaling prospects of quantum computers. As a proof-of-concept demonstration of “all-pass readout,” we design and fabricate an all-pass readout resonator that demonstrates insertion loss below 1.17 dB at the readout frequency and a maximum insertion loss of 1.53 dB across its full bandwidth for the lowest three states of a transmon qubit. We demonstrate qubit readout with an average single-shot fidelity of 98.1% in 600 ns; to assess the effect of larger dispersive shift, we implement a shelving protocol and achieve a fidelity of 99.0% in 300 ns.

关键词： Circuit quantum electrodynamics Optoelectronics Quantum error correction Quantum information with solid state qubits Quantum measurements Superconducting quantum optics Superconducting devices Microwave techniques

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Leveraging Topic Modeling and Extractive Summarization for Unlocking Insights from NeurIPS Papers

Leveraging Topic Modeling and Extractive Summarization for U...

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IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Sheikh Sharfuddin Mim Doina Logofatu Gabriel Guerrero-Contreras Inmaculada Medina-Bulo Department of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany Department of Computer Engineering University of Cadiz Cadiz Spain

ISBN: (数字)9798350368130

ISBN: (纸本)9798350368147

Efficiently comprehending vast volumes of text, particularly in scientific literature, is a formidable challenge for researchers. To alleviate this burden, this paper investigates the application of topic modeling and extractive summarization techniques to analyze and distill valuable insights from scientific documents. Focusing on the NeurIPS dataset, which encompasses a diverse array of scientific papers, we employ techniques such as Latent Dirichlet Allocation (LDA) and probabilistic Latent Semantic Analysis (pLSA) to categorize documents based on their content. Subsequently, we implement a domain-specific extractive summarizer that leverages Convolutional Neural Networks (CNNs) for feature extraction and semantic segmentation. The summarizer is trained to identify and rank sentences based on relevance, using binarized ROUGE-2 scores as a metric. Our results demonstrate the effectiveness of these methods in condensing scientific documents, facilitating quicker information retrieval and understanding. The paper also compares the performance of LDA and pLSA, highlighting LDA’s superiority in topic coherence and generalizability. By leveraging these methods, we aim to demonstrate their efficacy in processing large volumes of text, facilitating enhanced information retrieval and understanding in the realm of scientific literature.

关键词： Measurement Technological innovation Semantic segmentation Semantics Focusing Information retrieval Feature extraction

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The Efficacy of Deep Learning-Based Mixed Model for Speech Emotion Recognition

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computers, Materials & Continua 2023年第1期74卷 1709-1722页

作者： Mohammad Amaz Uddin Mohammad Salah Uddin Chowdury Mayeen Uddin Khandaker Nissren Tamam Abdelmoneim Sulieman Department of Computer Science and Engineering BGC Trust University BangladeshChittagong4381Bangladesh Centre for Applied Physics and Radiation Technologies School of Engineering and TechnologySunway UniversityBandar SunwaySelangor47500Malaysia Department of Physics College of SciencesPrincess Nourah bint Abdulrahman UniversityP.O Box 84428Riyadh11671Saudi Arabia Department of Radiology and Medical Imaging Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Human speech indirectly represents the mental state or emotion of *** use of Artificial Intelligence(AI)-based techniques may bring revolution in this modern era by recognizing emotion from *** this study,we introduced a robust method for emotion recognition from human speech using a well-performed preprocessing technique together with the deep learning-based mixed model consisting of Long Short-Term Memory(LSTM)and Convolutional Neural Network(CNN).About 2800 audio files were extracted from the Toronto emotional speech set(TESS)database for this study.A high pass and Savitzky Golay Filter have been used to obtain noise-free as well as smooth audio data.A total of seven types of emotions;Angry,Disgust,Fear,Happy,Neutral,Pleasant-surprise,and Sad were used in this ***,Fundamental frequency,and Mel Frequency Cepstral Coefficient(MFCC)have been used to extract the emotion features,and these features resulted in 97.5%accuracy in the mixed LSTM+CNN *** mixed model is found to be performed better than the usual state-of-the-art models in emotion recognition from *** also indicates that this mixed model could be effectively utilized in advanced research dealing with sound processing.

关键词： Emotion recognition Savitzky Golay fundamental frequency MFCC neural networks

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On the Relationship of Optimal State Feedback and Disturbance Response Controllers

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IFAC-PapersOnLine 2023年第2期56卷 7503-7508页

作者： Runyu (Cathy) Zhang Yang Zheng Weiyu Li Na Li School of Engineering and Applied Science Harvard University Department of Electrical and Computer Engineering University of California San Diego

This paper studies the relationship between state feedback policies and disturbance response policies for the standard Linear Quadratic Regulator (LQR). For open-loop stable plants, we establish a simple relationship between the optimal state feedback controller u t  = K * x t and the optimal disturbance response controller u t = L (H) *;1 w t-1 +...+ L (H) *;1 w t-H with H-order. Here x t , w t , u t stands for the state, disturbance, control action of the system, respectively. Our result shows that L (H) *,1 is a good approximation of K * and the approximation error ∥ K * — L (H) *;1 ∥ decays exponentially with H. We further extend this result to LQR for open-loop unstable systems, when a pre-stabilizing controller K 0 is available.

关键词： Linear Quadratic Regulator State Feedback Control Disturbance Response Control

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Employing Deep Neural Networks for Real-Time Anomaly Detection and Mitigation in IoT-Based Smart Grid Cybersecurity Systems 3

Employing Deep Neural Networks for Real-Time Anomaly Detecti...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Quraishi, Aadam Rusho, Maher Ali Prasad, Anurag Keshta, Ismail Rivera, Richard Bhatt, Mohammed Wasim Intervention Treatment Institute Research HoustonTX United States University Of Colorado Boulder Department Of Lockheed Martin Engineering Management BoulderCO United States Gurugram India College Of Applied Sciences AlMaarefa University Computer Science And Information Systems Department Riyadh Saudi Arabia Escuela Politécnica Nacional Department Of Informatics And Computer Science Quito Ecuador Model Institute Of Engineering And Technology J&K Jammu India

ISBN: (纸本)9798350318609

This research introduces a novel anomaly detection framework for IoT -based Smart Grid Cybersecurity Systems. Leveraging autoencoders, LSTM networks, GANs, SOMs, and transfer learning, our approach achieves superior precision, recall, and execution time compared to existing methods. Visualizations and an ablation study further validate the method's efficiency, emphasizing the critical roles of attention mechanisms and transfer learning. This comprehensive solution addresses the dynamic challenges of smart grid cybersecurity, offering a versatile and adaptive anomaly detection mechanism for real-world applications. This indicates the real-time efficacy of our anomaly detection method. Through our study of ablation and all aspects of computing, we discovered that attention processes and transfer learning facilitate faster problem solving in a dynamic smart grid. Our method is distinct and adaptable enough to address every problem arising from the discovery of anomalies in IoT-driven Smart Grid Cybersecurity Systems. © 2024 IEEE.

关键词： Anomaly detection

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