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6th International Conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

作者： Dora, Prajna Harini, N. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9783031813351

Electric vehicle(EV) range prediction is pivotal for adoption, affecting route planning, charging, and user confidence. A specifically curated dataset, formed via surveys and simulations, would provide in-depth specifics about EV usage customized for India. To broaden its scope, it could employ statistical techniques alongside a distinctive fusion of primary data and Probabilistic Conditional Generative Adversarial Networks (PCGANs). Despite this, traditional models like Artificial Neural Networks (ANN) struggle with inherent EV range uncertainty. Our research introduces Multi-Modal PCGANs (MM-PCGANs) to forecast EV range, offering a range of predictions with associated probabilities, expressing the model’s uncertainty based on input features. Through rigorous experimentation with our enhanced dataset, the MM-PCGANs model demonstrated superior performance, showcasing a 0.73% higher R2 score and a significant 63.89% lower Mean Squared Error (MSE) compared to the baseline ANN model. These findings highlight the accuracy of our approach in predicting EV range, emphasizing the efficacy of probabilistic generative models in alleviating range anxiety and delivering reliable predictions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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DISTILLING MONOLINGUAL AND CROSSLINGUAL WORD-IN-CONTEXT REPRESENTATIONS

arXiv

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arXiv 2024年

作者： Arase, Yuki Kajiwara, Tomoyuki School of Computing Tokyo Institute of Technology Japan Graduate School of Science and Engineering Ehime University Japan

In this study, we propose a method that distils representations of word meaning in context from a pretrained masked language model in both monolingual and crosslingual settings. Word representations are the basis for context-aware lexical semantics and unsupervised semantic textual similarity (STS) estimation. Different from existing approaches, our method does not require human-annotated corpora nor updates of the parameters of the pre-trained model. The latter feature is appealing for practical scenarios where the off-the-shelf pre-trained model is a common asset among different applications. Specifically, our method learns to combine the outputs of different hidden layers of the pre-trained model using self-attention. Our auto-encoder based training only requires an automatically generated corpus. To evaluate the performance of the proposed approach, we performed extensive experiments using various benchmark tasks. The results on the monolingual tasks confirmed that our representations exhibited a competitive performance compared to that of the previous study for the context-aware lexical semantic tasks and outperformed it for STS estimation. The results of the crosslingual tasks revealed that the proposed method largely improved crosslingual word representations of multilingual pre-trained models. © 2024, CC BY.

关键词： Semantics

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Multimodal Learning in Image Processing

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Computers, Materials & Continua 2025年第2期82卷 3615-3618页

作者： Zhixin Chen Gautam Srivastava Shuai Liu School of Educational Science Hunan Normal UniversityChangsha410081China Institute of Interdisciplinary Studies Hunan Normal UniversityChangsha410081China Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Research Centre for Interneural Computing China Medical UniversityTaichung406040Taiwan Institute of Engineering and Technology Chitkara UniversityChandigarh140401India

1 Introduction onMultimodal Learning in Image Processing IP(Image processing),as a classical research domain in computer application technology,has been researched for *** is one of the most important research directions in computer vision,which is the basis for many current hotspots such as intelligent transportation/education/industry,*** image processing is the strongest link for AI(artificial intelligence)applying to real world application,it has been a challenging research field with the development of AI,from DNN(deep convolutional network),Attention/LSTM(long-short term memory),to Transformer/Diffusion/Mamba based GAI(generated AI)models,e.g.,GPT and Sora[1].Today,the description ability of single-model feature limits the performance of image *** comprehensive description of the image is required to match the computational performance of current large scale models.

关键词： Image computer LSTM

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Adaptive Emotion Cause Pair Detection using Capsule Network

Adaptive Emotion Cause Pair Detection using Capsule Network

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Advanced computing Technologies (ICoACT), International Conference on

作者： S Baghavathi Priya Shyam Ganesh K Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Detecting the causes behind emotions in textual data is crucial for enhancing applications such as dialogue systems, customer service, and mental health assessments. Existing methods, often based on deep learning and machine learning, tend to focus on emotion classification but fall short in pinpointing the causes of those emotions within varied contexts. Proposed is a model that identifies the causes of emotions by analyzing textual data, dynamically adapting to varied emotional expressions in different contexts. The model integrates DistilBERT for generating contextual embeddings, along with a Bi-LSTM and Capsule Network to capture both sequential dependencies and hierarchical relationships in conversations. To achieve efficiency and scalability, distillation is applied, resulting in a lightweight model which ensures both efficiency and adaptability, allowing it to be suitable for real-world deployment in resource-constrained environments. This approach improves the precision of emotion cause detection being optimized for performance in dynamic, real-time scenarios.

关键词： Deep learning Adaptation models Emotion recognition Scalability Oral communication Mental health Encoding Data models Real-time systems Context modeling

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Voice Over Internet Protocol: Custom SIP Server with QoS Optimization for Real-Time Communication

Voice Over Internet Protocol: Custom SIP Server with QoS Opt...

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

作者： B. V. Dinesh Krishna P. Yogendrasai S. Rajeev Shinu M Rajagopal Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

VoIP is a revolutionary technology for transmission of voice over internet protocol than from plain telephony systems. But current VoIP solutions have some important issues which are packet loss, latency, and jitter especially when the network is in motion. This work outlines the design, implementation and performance of a locally developed VoIP system which includes a SIP Server Node, Sender Node and a Receiver Node. The SIP Server Node was designed nearly from the ground up to handle session initiation, packet forwarding, and adaptive QoS control. Both Sender and Receiver Nodes had facilities to record, compress and playback audio. The system's performance was evaluated in a LAN environment, achieving significant improvements in Quality of Service (QoS) metrics: achieving low latency under 150 ms, 95 % packet recovery and achieving a good audio quality with the MOS of 4.2. Evaluating the obtained results, it can be concluded that the proposed system is efficient in handling the issues of the packet-switched network and providing high-quality real time voice communication.

关键词： Packet loss Quality of service Receivers Jitter Real-time systems Encryption Servers Security Internet telephony Optimization

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Nova: Revolutionizing Agile Project Management with Scrum and Kanban Integration for Enhanced Team Collaboration and Productivity

Nova: Revolutionizing Agile Project Management with Scrum an...

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Global Conference for Advancement in Technology (GCAT)

作者： Poluri Manaswini Sajjad Shaik Siddhant Ashwani Roshni M Balakrishnan Shinu M Rajagopal Dept. of Computer Science Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

In the realm of software project management, Agile methodologies such as Scrum and Kanban have become quintessential for fostering adaptability and efficiency. "Nova," a novel project management tool, embodies these methodologies to offer a user-friendly interface that supports the dynamic needs of modern software development teams. This paper explores Nova’s unique approach that integrates the foundational principles of Agile which are Scrum, and Kanban, thereby facilitating a seamless and effective management platform. Through a comparative analysis with traditional project management tools, this study evaluates how Nova enhances team collaboration, project tracking, and outcome predictability. Emphasizing its ease of use and adaptability, the research highlights Nova’s potential to improve project delivery times and overall team productivity.

关键词： Productivity Collaboration Agile project management Software Scrum (Software development)

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Complex Permeability Determination for EE Core Based on Flexible PCB Fixture and Magnetic Flux Path Length Analysis 2

Complex Permeability Determination for EE Core Based on Flex...

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2nd China Power Supply Society Electromagnetic Compatibility Conference, CPEMC 2024

作者： Jie, Huamin Zhao, Zhenyu Chang, Yongqi Wang, Changdong Jiang, Shiqi Gao, Richard Xian-Ke See, Kye Yak School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering National Univeristy of Singapore Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Institute of High Performance Computing A*STAR Singapore Singapore

ISBN: (纸本)9798331509552

The complex permeability of magnetic cores acts as a vital factor in electromagnetic interference (EMI) filtering choke design and optimization. Many efforts have been reported for toroid cores, but fewer target those with other shapes, such as EE, EI, UU, and UI cores. Owing to their commonly used in planar EMI filters, determining the complex permeability of them is of significance. Targeting EE cores, this paper presents a flexible printed circuit board (PCB)-based fixture adapter tailored designed for performing the measurement, while the corresponding core permeability can be calculated through magnetic flux path length analysis. The experiments validate the effectiveness and accuracy of the proposed methods, spanning a wide frequency range up to 120 MHz. ©2024 IEEE.

关键词： Magnetic flux

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Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated IoT Prediction 3

Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Varun, P. Sunitha, T. Nagalingam, Mythili Nithiya, P. Selvakumaran, S. Mohanaprakash, T.A. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India St. Joseph's Institute of Technology OMR Department of Computer Science and Engineering Chennai600119 India Rajalakshmi Institue of Technology Department of Artificial Intelligence and Data Science Chennai India Department of Artificial Intelligence and Data Science Velammal Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institue of Technology Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. Smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LSTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities. © 2024 IEEE.

关键词： Solar power generation

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Sentiment Analysis on Telugu Text Translated from English Using NLP and ML

Sentiment Analysis on Telugu Text Translated from English Us...

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

作者： A.H.N Karthik Ch. Aneesh Gembali Saumik K.V.V Varun Kavitha C.R. Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This research study mainly uses Natural Language Processing (NLP) and Machine Learning. The general approach of this tool is to apply sentiment analysis in Telugu Text translated from English. The Technique contains many preprocessing steps, for example, tokenization of texts, normalization and noise removal for translating English text into Telugu which then will use machine translation services. It is followed by sentiment analysis of the statement using NLP models and machine learning approaches and classified them as; positive, negative, or neutral. The main objective of this study is to enhance the accuracy of sentiment identification with non-English language. Environments and also to develop rigorous sentiment analysis tools optimized for Telugu, which are designed as to the language and context-related difficulties to be taken into account. Additionally, the research examines analyses the impact of bilingual datasets on increasing translation accuracy and sentiment classification, while also tackling the obstacles given by idiomatic idioms and context-specific interpretations in Telugu Language.

关键词： Sentiment analysis Translation Accuracy Noise Machine learning Tokenization Object recognition Machine translation Internet of Things Data communication

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Novel Framework for Weather Prediction using Genetic Algorithm-based Model Tuning

Novel Framework for Weather Prediction using Genetic Algorit...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Pakala Venuteja Akkina Aateesh Dhiyaaram S Pandiri Bhaskar Praneeth Jeyakumar G Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidhyapheetam India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Machine learning model performances are very sensitive to hyperparameters so it is very crucial to have hyperparameter tuning. One of the focuses of the work presented in this paper is to assess the effect of hyperparameter optimization techniques, such as grid search, random search, and genetic algorithms (GA), on weather prediction models. So, for this, this study implements five machine learning models namely linear regression, random forest, artificial neural network (ANN), feedforward neural network (FNN) and gradient boosting using a dataset of a global weather repository. The study finds that in ANN, the GA performs best in terms of finding optimal hyperparameter values to predict the time series with the minimum Mean Squared Error (MSE) and maximum R-Squared (R 2 ) values. The impact of different hyperparameters of the model, namely learning rate, number of neuron layers, and number of neurons on the model performance is also studied. The study also discusses the benefit of GA for exploring the huge and intricate hyperparameter space and achieving conspicuous performance enhancements over conventional ways. These results highlight the role of GAs in increasing prediction accuracy which can be used in other tasks of machine learning.

关键词： Neurons Linear regression Weather forecasting Machine learning Artificial neural networks Predictive models Tuning Random forests Optimization Genetic algorithms

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