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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Joshi, Sudarshan Bachkar, Akshay Awaje, Omkar Bhoir, Rhutuj Savitribai Phule Pune University Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350348460

The paper explores various techniques for identifying multilingual text similarities and basic image similarities, covering a wide array of methods and opportunities for comparing and evaluating textual and diagrammatic content. By delving into these categories and studying the pros and cons of various methods, the survey equips researchers, practitioners, and enthusiasts in the field of NLP with a deeper understanding of text similarity *** paper delves into tokenization, vectorization, and similarity metrics, among other approaches. It also discusses modelling techniques, such as LSA and LDA, text alignment algorithms, cosine similarity, BERT, and optimization techniques. The survey categorizes these techniques into corpus-based, knowledge-based, semantic, term-based, and character-based similarities. The survey aims to provide a comprehensive overview of the methods available for checking multilingual text similarity identification and its significance in contemporary text analysis. Index Terms - Similarity Techniques, Machine learning, Natural Language Processing. © 2024 IEEE.

关键词： Image Similarity Multilingualism Natural Language Processing Semantic Analysis Similarity Techniques

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Leveraging ML Techniques for Image-based Freshness Index Prediction of Fruits and Vegetables 5

Leveraging ML Techniques for Image-based Freshness Index Pre...

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5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Gokhale, Atharva Chavan, Ameya Sonawane, Sheetal Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9781665475242

Freshness is a prime factor of consideration when purchasing consumables like fruits and vegetables. Studies have proven that computer Vision can be successfully involved in classifying fresh and stale fruits and vegetables and measuring their freshness to some extent. This work attempts to determine and analyze the freshness of fruits and vegetables from their images by proposing a Machine Learning methodology. The entire study was divided into two steps. The first step focused on obtaining classification between images of fresh and stale fruits and vegetables. For this, we trained the ConvNeXt model on an open-source imagery dataset consisting of 12 classes, and it proved efficient by achieving an accuracy of 99.77%. The second step focused on analyzing how fresh a particular fruit or vegetable is from its image. We achieved this by using an open-source dataset of tomato images and extracting features specific to the texture, shape, and colour from these images. Further, we trained classification models on these extracted features and presented the results as quantitative measures with scores of 10 for each of these three factors. Thus, we attempted to achieve an in-depth freshness analysis by grading the images based on these three critical factors while defining freshness. © 2023 IEEE.

关键词： computer vision

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Real-Time Electrochemical Model-Based BMS Control for Mitigating Li-Plating and Extending Battery Life

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7403-7419页

作者： Qasem, Mohammad Haddadin, Mariana Yassin, Yazan Ratrout, Sadam Chen, Chengxiu Stoyanov, Stoyan Al-Hallaj, Said Krishnamurthy, Mahesh Southern Methodist University Department of Electrical and Computer Engineering DallasTX75205 United States Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States Beam Global BroadviewIL60155 United States

It is well-known that lithium plating significantly reduces the capacity of Li-ion batteries, particularly at elevated charging rates, high state of charge (SoC), and low temperatures. This study presents a simplified electrochemical battery model incorporating side reactions for real-time (RT) applications. The proposed model is implemented on a battery management system (BMS) using the TI TMS320F28335 DSP, the model’s internal and external states are validated through extensive experimental tests against the high-fidelity P2D model by Gamma technology. The results demonstrated the model’s ability to accurately depict the behavior of a commercially available type 21 700 cylindrical cell while minimizing execution power consumption. The lithium plating/stripping model is examined and compared with the actual voltage plateau characteristics observed in practical testing. Finally, various controllers are designed and implemented into the BMS to optimize the charging current and eliminate plating without significantly increasing charging duration. The battery demonstrated an increase in capacity of 8.3% when using the proposed charging control algorithm compared with the common constant current (CC)–constant voltage (CV) technique, with a charging time increase of less than 30 min, resulting in a 17% improvement in SoH. This demonstrates the effectiveness of enhancing battery performance and longevity with minimal additional charging time. © 2015 IEEE.

关键词： Lithium-ion batteries

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NLP based Text Summarization of Fintech RFPs 2

NLP based Text Summarization of Fintech RFPs

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Patil, Krutika Badamikar, Medha Sonawane, Sheetal Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9781665491990

In today's day and age, where huge quantities of textual data are generated every second, it has become difficult to keep ourselves abreast with new information. Documents in the financial sector tell a quantitative story. The qualitative or language content that accompanies financial statements is an essential component of the information set used by financial market participants for monitoring and stewardship. This necessitates the development of efficient technological methods for leveraging the existence of these massive amounts of textual data. Reading financial documents such as annual reports is extremely time-consuming and thus corporations have to allocate precious human capital to understand, analyze and derive insight from these reports. As a result, automatic summarization techniques can make this task simpler by enabling access to a smaller but informative portion of a given document. In this work, a system utilizing NLP techniques for summarizing financial documents based on queries provided by the user, is presented. This system overcomes the challenges posed by existing section-based summarization models. As machine learning methods have proven to be effective on downstream tasks such as text summarization, the intent of this work is to take advantage of these methods for effective extractive summarization and generate human-like summaries. The proposed model categorizes phrases according to their relevance by leveraging the strength of unsupervised clustering approaches, and it gets a ROUGE-1 score of 46%. © 2023 IEEE.

关键词： Natural language processing systems

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Short-Term Power Load Forecasting Method Based on GRU-Transformer Combined Neural Network Model

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Journal of Computing and Information technology 2024年第1期32卷 1-14页

作者： Mao, Weiwei Yu, Suping Chen, Wenqing College of Computer and Information Engineering Luoyang Institute of Science and Technology Luoyang China School of Electrical Engineering and Automation Luoyang Institute of Technology Luoyang China

Load Forecast (LF) is an important task in the planning, control and application of public power systems. Accurate Short Term Load Forecast (STLF) is the premise of safe and economical operation of a power system. In the research of short-term power load forecasting, machine learning and deep learning are the most popular methods at present, but there still exists a problem that the single and simple structure of power load forecasting model leads to low accuracy of load forecasting. In order to improve the accuracy of STLF, a Gated Cycle Unit (GRU)-Transformer combined neural network model is proposed. Transformer encoder structure is used as feature extractor to mine the complex mapping relationships between the input features and load. The advantage of self-attention mechanism is used to solve the problem of information loss of long sequences in short-term power load forecasting. At the same time, the multivariate time series model of GRU is used for model training. The experimental results on the power load data set of a certain region in southwest China and Panama City show that the proposed combined model prediction method has higher accuracy than those proposed in other literatures, which further proves its feasibility and superiority. © 2024, University of Zagreb Faculty of Electrical engineering and Computing. All rights reserved.

关键词： Neural network models

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Localize the Copy-Move Forged Region of an Image Using Improved SIFT

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SN computer Science 2024年第1期5卷 71页

作者： Dhanaraj, Rachel Selva Sridevi, M. Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli India

With the advancement in technology, digital images have become a very popular source of information. But this source of information is also being used to mislead people and society by altering the original image. One of the ways through which digital images are tampered with is the copy-move forgery method, wherein a part of an image is copied and pasted into another area inside the same image. To avoid and prevent any kind of anomalies, this paper focuses on ways to detect this copy and move forgery in an image using the keypoint-based approach in an efficient manner. The methodology involves detecting key points using Improved Scale Invariant Feature Transform (SIFT) based on which features are identified. Within the isolated descriptors, the deployed K-Nearest Neighbor (K-NN) classifier determines the closest match. Now in order to remove the outliers present in the features, Hierarchical Clustering is used to cluster similar descriptors. Experimental result has proved this methodology to be effective in detecting the forgery in an image for different kinds of related transformations. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Copy-move forgery Hierarchical clustering K-NN classifier Scale invariant feature transform

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SMILES2DTA: a CNN-based approach for identifying drug candidates and predicting drug-target binding affinity

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Neural Computing and Applications 2024年第4期37卷 2891-2910页

作者： Mukit, Hasanul Hossain, Sayeed Farabi, Mirza Milan Chowdhury, Mehrab Zaman Pritom, Ahmed Iqbal Rana, Humayan Kabir Department of Computer Science and Engineering Green University of Bangladesh Narayanganj-1461 Dhaka Bangladesh Department of Computer Science The University of Alabama at Birmingham BirminghamAL United States Department of Computer Science and Engineering Islamic University of Technology Gazipur Bangladesh

Computational approaches can speed up the drug discovery process by predicting drug-target affinity, otherwise it is time-consuming. In this study, we developed a convolutional neural network (CNN)-based model named SMILES2DTA (Simplified Molecular Input Line Entry System to Drug-Target Affinity) for predicting the binding affinity between a drug and a target protein. The model utilizes CNNs to process sequences of both drug SMILES and target proteins. SMILES2DTA generates multiple sequences from a single drug SMILES sequence, validates them based on Lipinski’s rule of five, and assesses their binding affinity against a target protein sequence. We evaluated our model using publicly available datasets and compared its performance to state-of-the-art methods. The results showed that SMILES2DTA outperformed the existing methods and demonstrated improved accuracy, mean squared error, and area under the precision-recall curve. SMILES2DTA has the potential to speed up the drug discovery process by reducing the time and cost complexity of identifying effective drugs. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Drug discovery

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A Novel Method Based on CNN-LSTM to Characterize Knee Osteoarthritis from Radiography

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Proceedings of the National Academy of Sciences India Section B - Biological Sciences 2024年第2期94卷 423-438页

作者： Malathi, S.Y. Bharamagoudar, Geeta R. Department of Computer Science and Engineering KLE Institute of Technology Hubballi Karnataka Hubballi India

This is particularly true for the senior population, whose quality of life has been drastically reduced as a result of the increasing incidence of several health problems. Over 27 million people in the United States suffer from osteoarthritis of the knee (OAK), a painful condition that may severely limit mobility. When the articular cartilage between the tibia and femur in the knee is damaged, osteoarthritis of the knee develops. OAK symptoms include a loss of mobility and the inability to walk normally due to knee pain, detected using an X-ray. We detail here a novel method of evaluating the severity of knee osteoarthritis (OA) by X-ray analysis. Modern methods are comprised of pre-processing, feature extraction using a convolutional neural network (CNN), and classification with latent semantic modeling (LSM) (LSTM). Data from the osteoarthritis initiatives (OAI) database, which is available to the public, was utilized to evaluate the methodology proposed. The current method has been shown to be effective, and the OAI database has information on KL grade assessment for both knees. OAK is the subject of state-of-the-art, global observational investigation by experts using a program called OAI. This collection was created to serve as a one-stop shop for researchers seeking the scholarly materials they need to systematically examine OA indicators as a possible endpoint for the advanced stages of the illness. The statistics reveal a mean accuracy of 100%. When compared to earlier deep learning approaches, these outcomes are much superior. © The Author(s), under exclusive licence to The National Academy of Sciences, India 2024.

关键词： Deep learning Knee osteoarthritis Radiography X-ray

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A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

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computers, Materials & Continua 2025年第5期83卷 2449-2479页

作者： Vijaya Krishna Akula Tan Kuan Tak Pravin Ramdas Kshirsagar Shrikant Vijayrao Sonekar Gopichand Ginnela Department of Information Technology G.Narayanamma Institute of Technology and Science for WomenHyderabad500104India Engineering Cluster Singapore Institute of Technology10 Dover DriveSingapore138683Singapore Department of Electronics and Telecommunication Engineering J.D.College of Engineering&ManagementNagpur441501India Department of Computer Science and Engineering J.D.College of Engineering&ManagementNagpur441501India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of *** paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and *** results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT *** are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient *** algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node *** study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.

关键词： Internet of things trust energy marine predators algorithm(MPA) differential evolution(DE) nodes throughput lifetime

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DIFFAR: DENOISING DIFFUSION AUTOREGRESSIVE MODEL FOR RAW SPEECH WAVEFORM GENERATION 12

DIFFAR: DENOISING DIFFUSION AUTOREGRESSIVE MODEL FOR RAW SPE...

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12th International Conference on Learning Representations, ICLR 2024

作者： Benita, Roi Elad, Michael Keshet, Joseph Department of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa Israel Department of Computer Science Technion - Israel Institute of Technology Haifa Israel

Diffusion models have recently been shown to be relevant for high-quality speech generation. Most work has been focused on generating spectrograms, and as such, they further require a subsequent model to convert the spectrogram to a waveform (i.e., a vocoder). This work proposes a diffusion probabilistic end-to-end model for directly generating the raw speech waveform. The proposed model is autoregressive, generating overlapping frames sequentially, where each frame is conditioned on a portion of the previously generated one. Hence, our model can effectively synthesize an unlimited speech duration while preserving high-fidelity synthesis and temporal coherence. We implemented the proposed model for unconditional and conditional speech generation, where the latter can be driven by an input sequence of phonemes, amplitudes, and pitch values. Working directly on the waveform has some empirical advantages. Specifically, it allows the creation of local acoustic behaviors, like vocal fry, which makes the overall waveform sounds more natural. Furthermore, the proposed diffusion model is stochastic and not deterministic;therefore, each inference generates a slightly different waveform variation, enabling abundance of valid realizations. Experiments show that the proposed model generates speech with superior quality compared with other state-of-the-art neural speech generation systems. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Diffusion

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