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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Ayodele A Periola Innocent E. Davidson French-South African Institute of Technology Dept. of Electrical Electronic and Computer Engineering Cape Peninsula University of Technology South Africa

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

Renewable energy systems have been recognized to play an important role in future environmentally friendly energy systems. An important source of renewable energy is solar power derived when solar panels intercept with incident solar radiation. The degradation effect of cloud obstruction has led to the consideration of space-based solar power systems. In space-based solar power systems, satellites with large solar panel payload transmit solar energy to earth-based rectennas. The deployment of multiple space based solar power satellites is necessary to ensure that more energy is derived from space based solar power. However, the realization of such a system requires the design of communication protocols between in-orbiting space-based power satellites. The use of such a system enables multiple receiving antenna ground stations to concurrently receive power from solar power satellites. The presented research aims to present the design of system protocols that enable the realization of this goal, considering heterogeneous solar power satellites and operational conditions. This is carried out while considering communications between in-orbiting space-based solar power satellites, along with satellite rectennas. Furthermore, performance analysis and investigations show that the use of the proposed solution enhances power accessibility and reduces deficit by an average of 18%.

关键词： Renewable energy sources Satellites Protocols Rectennas Prototypes computer architecture Solar energy Power systems Solar panels Solar radiation

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Analysis on Performance of Facial Expression Recognition using Conventional and Deep Learning Approaches 3

Analysis on Performance of Facial Expression Recognition usi...

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3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Jagadeesh, M. Baranidharan, B. Srm Institute of Science and Technology Dept. of Computer Science Tamilnadu Chennai603203 India

ISBN: (纸本)9780738146270

The ability to identify a person's inner thoughts through expressions before verbalizing is known as Facial Expression Recognition (FER). Nowadays, the phrase 'Human-computer Interaction' has become the most frequently searched keyword, especially in research on automatic depression diagnosis, psychiatric illness, false narratives, and suppressed emotions. The main objective of this survey is to give a clear view of automatic FER models. Also, this research work highlights the different problems encountered in the determination of facial characteristics. For better knowledge, first various emotion databases are listed with the range of emotions that each database has recognized. Further, this paper has been organized in such a way to provide a thorough and better discussion on the three phases of the facial expression recognition model. Then, various approaches are compared and adopted in image-based and video-based FER models from recent findings and finally, this research study has provided the right directions on how robust and challenging models for future research can be developed. © 2021 IEEE.

关键词： Support vector machines

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The Effect of Human v/s Synthetic Test Data and Round-tripping on Assessment of Sentiment Analysis Systems for Bias

arXiv

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arXiv 2024年

作者： Lakkaraju, Kausik Gupta, Aniket Srivastava, Biplav Valtorta, Marco Wu, Dezhi AI Institute University of South Carolina ColumbiaSC United States Dept. of Computer Science Netaji Subhas University of Technology Delhi India Dept. of Computer Science and Engineering University of South Carolina ColumbiaSC United States Dept. of Integrated Information Technology University of South Carolina ColumbiaSC United States

Sentiment Analysis Systems (SASs) are data-driven Artificial Intelligence (AI) systems that output polarity and emotional intensity when given a piece of text as input. Like other AIs, SASs are also known to have unstable behavior when subjected to changes in data which can make them problematic to trust out of concerns like bias when AI works with humans and data has protected attributes like gender, race, and age. Recently, an approach was introduced to assess SASs in a blackbox setting without training data or code, and rating them for bias using synthetic English data. We augment it by introducing two human-generated chatbot datasets and also considering a round-trip setting of translating the data from one language to the same through an intermediate language. We find that these settings show SASs performance in a more realistic light. Specifically, we find that rating SASs on the chatbot data showed more bias compared to the synthetic data, and round-tripping using Spanish and Danish as intermediate languages reduces the bias (up to 68% reduction) in human-generated data while, in synthetic data, it takes a surprising turn by increasing the bias! Our findings will help researchers and practitioners refine their SAS testing strategies and foster trust as SASs are considered part of more mission-critical applications for global use. © 2024, CC BY-NC-ND.

关键词： Sentiment analysis

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Sentiment Analysis on the Elevated Expressway in Bangladesh: A Machine Learning and Deep Learning Approach

Sentiment Analysis on the Elevated Expressway in Bangladesh:...

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Power, Electrical, and Electronics and Industrial Applications (PEEIACON), IEEE International Conference on

作者： Samiha Maisha Jeba Rajib Khan Md Shoaib Ahmed Tanjim Taharat Aurpa Satya Ranjan Dash Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Bangladesh Department of Computer Science Boise State University Boise Idaho USA Dept. of Educational Technology Bangabandhu Sheikh Mujibur Rahman Digital University Bangladesh School of Computer Applications KIIT University Odisha India

ISBN: (数字)9798331517984

ISBN: (纸本)9798331517991

User comments on social media or any video streaming platforms show how people feel about something, including different opinions, feelings, and problems of people interacting with it. It can assist people and organizations in making wellinformed choices by offering insightful information on how people feel about a topic or issue. The Elevated Expressway is a crucial infrastructure in Bangladesh and a controversial project that has sparked public debate and criticism. The 46.73 km road project connecting Hazrat Shahjalal International Airport to Kutubkhali on the Dhaka-Chittagong Highway has eased the commute time from the Airport to Farmgate from over an hour to 10 minutes. Regarding this project, People's comments on YouTube as a digital platform for voicing their opinions and contributing to a shared narrative that goes beyond traditional media. In this work, we examine users' sentiments about Elevated Expressway from YouTube comments, employing various Machine Learning (ML) algorithms and Deep Learning (ML) architecture. Based on these analyses, the BERT architecture arises as the most satisfying model, with an accuracy of 84.34% and a macro f1 score of 80.14% for classifying public sentiment as positive, negative, or neutral.

关键词： Deep learning Sentiment analysis Video on demand Social networking (online) Roads Organizations Airports Transformers Web sites Stakeholders

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Towards Adoption of Green Blockchain with Emphasis on Blockchain Type, Consensus Protocols, Data Sharding and Smart Contracts

Towards Adoption of Green Blockchain with Emphasis on Blockc...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Shraddha Phansalkar Deepa Mishra Neha Chaube Rahul Sonkamble Dept. of Computer Science & Engineering MIT Art Design and Technology University Pune Maharashtra

Blockchain and related Distributed Ledger Technologies (DLT) are anticipated to transform a the world of web from a centralised document-sharing platform to a comprehensive decentralised platform that facilitates the exchange of digital currency and supports autonomous management of digital assets. The central server is susceptible to attacks, distrust and collusion. If the web can assure reliable, safe, and responsible updates among independent participants without the need for a centralized server, the perception of a decentralised web can be re-instantiated. One of the essential technologies required to restore the openness of the internet while maintaining its security is distributed ledger technology (DLT). DLTs may now totally handle business and legal transactions online, creating a more trustworthy and accountable environment. Blockchain technology marks a major breakthrough by removing the need for a centralised trusted authority in a widely distributed network. Instead, a consensus must be reached among several sources of trust, based on an algorithm, that this transaction may be believed to be legitimate. The consensus algorithms in blockchain technology offer an immutable and permanent record of a transaction that is immutable, trustworthy and secured. Consensus algorithms are however energy consuming because of their computation heavy nature. This has been a biggest inhibition towards blockchain adoption. The energy needs for committing a blockchain transaction is also governed by whether it is a public/permissioned blockchain, its consensus algorithm, onchain-offchain data and the code complexity of smart contracts. The paper presents a state-of art evaluation of the current blockchain platforms and cryptos and evaluate them with the energy consumptions. Additionally, it also proposes a framework architecture of a green blockchain application. A green blockchain refers to the implementation of environmentally sustainable algorithms, tools and platforms

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Sentiment Analysis of Movie Reviews: Optimizing Predictions Through TF-IDF Vectorization

Sentiment Analysis of Movie Reviews: Optimizing Predictions ...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Mohd Irfan Anne Sreyas Venkatesam Hariharan Shanmugasundaram Mahendra Kumar Gourisaria Subrata Chowdhury Sudhansu Shekhar Patra Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad Telangana India Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India School of Computer Engineering Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India Dept. of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies(A) Chittoor Andhra Pradesh India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Sentiment analysis model is aimed to accurately interpret movie reviews, addressing challenges like sarcasm and noisy data. By integrating multiple machine learning classifiers Naive Bayes, Random Forest, K-Nearest Neighbors, Logistic Regression, Decision Tree, and XGBoost—the model enhances sentiment prediction accuracy. The Term Frequency-Inverse Document Frequency (TF-IDF) method is used to extract features from a dataset of 50,000 movie reviews. Performance is evaluated using accuracy, precision, recall, and F1-score, with Logistic Regression achieving the best results, including an highest accuracy of 84.33 % and an F1-score of 0.847. In comparison to traditional methods, this multi-classifier approach offers a deeper understanding of movie reviews and its influence on audience decisions. The study demonstrates the potential of using these machine learning techniques to provide real-time, scalable sentiment analysis, which is crucial for filmmakers and marketers seeking insights into audience satisfaction and box office performance.

关键词： Sentiment analysis Logistic regression Accuracy Reviews Predictive models Motion pictures Feature extraction Real-time systems Noise measurement Random forests

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Task-Oriented Communication with Out-of-Distribution Detection: An Information Bottleneck Framework

arXiv

引用

arXiv 2023年

作者： Li, Hongru Yu, Wentao He, Hengtao Shao, Jiawei Song, Shenghui Zhang, Jun Letaief, Khaled B. Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Task-oriented communication is an emerging paradigm for next-generation communication networks, which extracts and transmits task-relevant information, instead of raw data, for downstream applications. Most existing deep learning (DL)-based task-oriented communication systems adopt a closed-world scenario, assuming either the same data distribution for training and testing, or the system could have access to a large out-of-distribution (OoD) dataset for retraining. However, in practical open-world scenarios, task-oriented communication systems need to handle unknown OoD data. Under such circumstances, the powerful approximation ability of learning methods may force the task-oriented communication systems to overfit the training data (i.e., in-distribution data) and provide overconfident judgments when encountering OoD data. Based on the information bottleneck (IB) framework, we propose a class conditional IB (CCIB) approach to address this problem in this paper, supported by information-theoretical insights. The idea is to extract distinguishable features from in-distribution data while keeping their compactness and informativeness. This is achieved by imposing the class conditional latent prior distribution and enforcing the latent of different classes to be far away from each other. Simulation results shall demonstrate that the proposed approach detects OoD data more efficiently than the baselines and state-of-the-art approaches, without compromising the rate-distortion tradeoff. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Risk Prediction of Opioid Dependency Using Machine Learning Based on Electronic Health Record

Risk Prediction of Opioid Dependency Using Machine Learning ...

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computer science and Data engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Samaneh Madanian Sherry Dai Priyadarshini Natarajan Hamidreza Rasoulipanah Dept. of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand

Opioid abuse and dependence have emerged as a pressing global concern, posing significant challenges to public health and society. Early identification and prediction of opioid dependency represent crucial steps in mitigating its abuse impact on individuals and communities. The application of machine learning techniques to analyze medical data has opened new avenues for achieving this goal. While this field and the prediction is still in its infancy, our research explores the potential of several machine learning algorithms including LightGBM for this risk prediction. To tackle the inherent class imbalance in the MIMICIII dataset, we implemented the Synthetic Minority Oversampling Technique (SMOTE). We developed predictive models using four distinct algorithms: decision trees, random forests, support vector machines, and LightGBM. These models were meticulously evaluated to assess their performance. Ultimately, our findings revealed that the LightGBM model outperformed the other algorithms, demonstrating superior accuracy and achieving a higher Area Under the Curve value. This outcome underscores the potential of LightGBM as a valuable algorithm in the early prediction of the risk of opioid dependence, thereby offering substantial benefits to both patients and society at large.

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Captioning Based Image Using Euclidean Distance and ResNet-50

Captioning Based Image Using Euclidean Distance and ResNet-5...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： S. Visnu Dharsini M. Abdul Razak Smit Modi Palleti Karthikeya Reddy Sarthak Bhatnagar Dept. of Computer Science Engineering SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9798350333855

Past few years researchers have focused on using the attention and transformer model to access image insights and provide deep descriptions for an input image. Although these methods have shown greater improvementsover the regular methodology of using recurrent neural networks. But still, it is under the great influence of the gradient vanishing issue. Our way of tackling this issue is to minimize the use of gradient-intensive task and replace it with distance-mapping tasks. This means that our model predicts the output on the basis of “Euclidian Distance”. To accomplish this first we use pretrained neural network to extract features and then use K Nearest neighbor to cluster image with similar features together. Here the model is used to gather low level object information to generate relevant caption so that model can work on the top efficiency and also the generated caption is natural. To fulfill this requirement, we make use for our feature extractor model and clustering model to find the closest resembling image to our query image and return its most relevant captions

关键词： Recurrent neural networks Image edge detection Euclidean distance Predictive models Feature extraction Transformers Task analysis

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An Analytical Approach of Higher Order Electric Vehicle System Reduction Using Truncation Based Approximation

An Analytical Approach of Higher Order Electric Vehicle Syst...

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International Conference on Power Electronic Drives

作者： Sameer Singh V. P. Singh U. K. Yadav Akhilesh Mathur Suryansh Kumar Pandey Shailendra Kumar Krishna Murari Electrical Engineering M. National Institute of Technology Jaipur RJ India Electrical Engineering Indian Institute of Technology Bhilai Chhattisgarh India Dept. of Electrical Engineering and Computer Science The University of Toledo Ohio USA

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

In this research, reduction of fifth-order electric vehicle (EV) transfer function (TF) is accomplished by exploiting direct truncation method. The higher order TF (HOTF) of order five is approximated to obtain the reduced order transfer function (ROTF) of comparatively lower order. The truncation of numerator and denominator coefficients is done to determine the desired EV ROTF. The graphical representation of step, error, Nichols and Bode responses are presented in the support of EV ROTFs obtained for EV HOTF. The error indices data and time domain specifications data are also provided in tabulated form to prove the efficacy of proposed ROTFs.

关键词： Uncertainty Transfer functions Electric vehicles Power electronics Data models Time-domain analysis

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