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arXiv 2025年

作者： Fathollahzadeh, Pouya Mezouar, Mariam El Li, Hao Zou, Ying Hassan, Ahmed E. Department of Electrical and Computer Engineering Queen’s University KingstonONK7L 3N6 Canada School of Computing Queen’s University KingstonONK7L 3N6 Canada Department of Mathematics and Computer Science Royal Military College of Canada KingstonONK7K 7B4 Canada

In software engineering chatrooms, communication is often hindered by imprecise questions that cannot be answered. Recognizing key entities (e.g., programming languages and libraries) and user intent (e.g., learning or requesting a review) can be essential for improving question clarity and facilitating better exchange. However, existing research using natural language processing techniques often overlooks these software-specific nuances. In this paper, we introduce softwarE-specific Named entity recognition, Intent detection, and Resolution classification (sENIR), a labelling approach that leverages a Large Language Model to annotate entities, intents, and resolution status in developer chatroom conversations. To offer quantitative guidance for improving question clarity and resolvability, we build a resolution prediction model that leverages sENIR’s entity and intent labels along with additional predictive features. We evaluate sENIR on the DIsCO dataset using a subset of annotated chatroom dialogues. sENIR achieves an 86% F-score for entity recognition, a 71% F-score for intent detection, and an 89% F-score for resolution status classification. Furthermore, our resolution prediction model, tested with various sampling strategies (random undersampling and oversampling with sMOTE) and evaluation methods (5-fold cross-validation, 10-fold cross-validation, and bootstrapping), demonstrates AUC values ranging from 0.7 to 0.8. Key factors influencing resolution include positive sentiment and entities such as Programming Language and User variable across multiple intents, while diagnostic entities (e.g., Error Name) are more relevant in error-related questions. Moreover, resolution rates vary significantly by intent: questions about API Usage and API Change achieve higher resolution rates, whereas Discrepancy and Review have lower resolution rates. A Chi-square analysis confirms the statistical significance of these differences. © 2025, CC bY.

关键词： Classification (of information)

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Prediction of stock Price Using XG boost Integrated bi-Channel LsTM

Prediction of Stock Price Using XG Boost Integrated Bi-Chann...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Amey Adinath Choudhari Rajeswaran Ayyadurai Jayant Maruti Hudar v.K. Arthi Tasriqul Islam G Jayashree Hareesh JSPM's Rajashi Shahu College of Engineering Pune India IL Health & Beauty Natural Oils Co Inc California USA DBA BHS University Switzerland DBA in Data Science and AI PGDBM B.E. Electronics. Diploma in Computer Technology Navi Mumbai India Department of Management Studies Sri Ramakrishna Engineering College Coimbatore India Harvard University Cambridge Massachusetts USA Fahad Bin Sultan University Tabuk Saudi Arabia

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Investors' biggest problem is determining the appropriate stock price by studying financial information, which is a difficult process due to enormous and related inefficiencies. so, among the biggest challenges for investors is identifying a stock. Currently, it's very challenging to carry out research in the forecasts for the finance industry, including the stock market, currency value, and stock price. Due to its critical importance in financial consumers' decision-making, the prediction and examination of the stock price is yet another important research subject. A novel Xgboost Integrated bichannel Long/short-Term Memory (X-bLsTM) technique is implemented in this article to provide a sophisticated and perfect framework for the share market's price prediction The share value dataset is the first thing we collect to assess the way the suggested X-bLsTM strategy performs. Due to the existence of noisy data, min-max normalization is performed to preprocess the raw dataset. The efficiency of the proposed approach is assessed using several measures, and comparative between the proposed and current methods is also provided. The studies' findings show that the recommended approach outperforms alternative techniques for share value prediction.

关键词： Training Industries Electric potential Current measurement Decision making Noise measurement stock markets Forecasting Long short term memory Currencies

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Explainable deep stacking ensemble model for accurate and transparent brain tumor diagnosis

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computers in biology and Medicine 2025年 191卷 110166-110166页

作者： Haque, Rezaul Khan, Mahbub Alam Rahman, Hamdadur Khan, shakil siddiqui, Md Ismail Hossain Limon, Zishad Hossain swapno, s M Masfequier Rahman Appaji, Abhishek Department of Computer Science and Engineering East West University A 2 Jahurul Islam Ave Dhaka1212 Bangladesh Department of Management Information System Pacific State University 3424 Wilshire Blvd. 12th Floor Los Angeles CA90010 United States Department of Management of Information System International American University 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States Department of Business Analytics International American University 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States Department of Engineering/Industrial Management Westcliff University IrvineCA92614 United States Department of Computer Science Westcliff University IrvineCA92614 United States Department Of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur-2 Dhaka1216 Bangladesh Department of Medical Electronics Engineering B.M.S. College of Engineering Bangalore560019 India Maastricht University University Eye Clinic Maastricht Maastricht Netherlands

Early detection of brain tumors in MRI images is vital for improving treatment results. However, deep learning models face challenges like limited dataset diversity, class imbalance, and insufficient interpretability. Most studies rely on small, single-source datasets and do not combine different feature extraction techniques for better classification. To address these challenges, we propose a robust and explainable stacking ensemble model for multiclass brain tumor classification. To address these challenges, we propose a stacking ensemble model that combines EfficientNetb0, MobileNetv2, GoogleNet, and Multi-level CapsuleNet, using Catboost as the meta-learner for improved feature aggregation and classification accuracy. This ensemble approach captures complex tumor characteristics while enhancing robustness and interpretability. The proposed model integrates EfficientNetb0, MobileNetv2, GoogleNet, and a Multi-level CapsuleNet within a stacking framework, utilizing Catboost as the meta-learner to improve feature aggregation and classification accuracy. We created two large MRI datasets by merging data from four sources: braTs, Msoud, br35H, and sARTAJ. To tackle class imbalance, we applied borderline-sMOTE and data augmentation. We also utilized feature extraction methods, along with PCA and Gray Wolf Optimization (GWO). Our model was validated through confidence interval analysis and statistical tests, demonstrating superior performance. Error analysis revealed misclassification trends, and we assessed computational efficiency regarding inference speed and resource usage. The proposed ensemble achieved 97.81% F1 score and 98.75% PR AUC on M1, and 98.32% F1 score with 99.34% PR AUC on M2. Moreover, the model consistently surpassed state-of-the-art CNNs, vision Transformers, and other ensemble methods in classifying brain tumors across individual four datasets. Finally, we developed a web-based diagnostic tool that enables clinicians to interact with the proposed mo

关键词： Magnetic resonance imaging

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A transformer based real-time photo captioning framework for visually impaired people with visual attention

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Multimedia Tools and Applications 2024年第41期83卷 88859-88878页

作者： Muhammed Kunju, Abubeker Kiliyanal baskar, s. Zafar, sherin A R, bushara s, Rinesh A, shafeena Karim Kerala Kanjirapally India Faculty of Engineering Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer and Electronics Engineering SEST- Jamia Hamdard New Delhi India Department of Electronics and Communication Engineering KMEA College of Engineering Kerala Cochin India Department of Computer Science and Engineering V.S.B College of Engineering Technical Campus Coimbatore India

In recent years, transformer-based photo captioning frameworks plays a crucial role in improving individuals’ overall well-being, self-reliance, and inclusivity by giving them access to visual content via written and voiced explanations. This research investigates a two-layer transformer architecture to capture extensive relationships in visual data effectively. Using a two-layer transformer design, visual attention mechanism enables the model to autonomously identify relevant regions within a picture while generating a particular word within the anticipated caption. To get the features from the images, a pre-trained Inception v3 model is used, and the feature maps are extracted from the final convolutional block. The dataset used in this research was Flick8k, Flick30k, among bespoke datasets. The model was trained on a TPU platform and implemented on a Raspberry Pi 4b graphics processing unit. To assess the efficacy of the two-layer transformer model for photo captioning, a series of experiments were undertaken using benchmark datasets and with state-of-the-art models. The quality of the generated captions was evaluated using quantitative criteria such as bLEU, bLEUR, ROUGE and F1 score. Furthermore, a series of qualitative assessments were conducted, employing human evaluators, to measure the overall coherence, and fluency of the captions. © The Author(s), under exclusive licence to springer science+business Media, LLC, part of springer Nature 2024.

关键词： computer vision

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vegetable supply Chain Using smart Contract 2

Vegetable Supply Chain Using Smart Contract

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Arivarasan, s. Nithish, D. Rahul, P. senthurvel, K. Aravind, venu V.S.B. Engineering College Stream of Computer Science and Engineering Karur India V.S.B. Engineering College R Stream of Computer Science and Engineering Karur India

ISBN: (纸本)9798350368109

As for therapeutic, ethical and legal restrictions regarding vegetables' accessibility today and concerning registration, matching of the donor-recipient pair, vegetable removal, delivery and transplantation, the needs and challenges vegetable supply and transplantation systems offer today are diverse. This means that to enhance the patient experience and gain their trust, demands for vegetable supply and transplantation that is just and efficient the requires an end-to-end system. Thus, in this study, we give a new solution concept of vegetable supply system which is Decentralized, safe, Transparent, Auditable, Confidential. in vegetable supply and transplantation buyer through sha256 private blockchain. We also list six algorithms and describe the implementation, testing, and validation of all the algorithms. © 2024 IEEE.

关键词： smart contract

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Clock synchronization Protocols and Models for Wireless sensor Networks 16

Clock Synchronization Protocols and Models for Wireless Sens...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Geetha, s. Latha, P. vardhini, A Raajya suganya, R. Nidhya, M.s. V.S.B Engineering College Bangalore India School of Computer Applications Dayananda Sagar University Bangalore India

ISBN: (纸本)9798331505264

Clock synchronization is a critical aspect in the operation of Wireless sensor Networks (WsNs), ensuring that sensor nodes maintain accurate time for coordinated communication and data processing. various clock synchronization algorithms have been developed to address the synchronization challenges in WsNs, aiming to align the clocks of distributed nodes efficiently. These algorithms play a crucial role in achieving synchronization by compensating for clock drift, which can significantly impact the accuracy of timekeeping among sensor nodes. Timing constraints are fundamental in WsNs, as they influence network performance and data transmission reliability. Addressing timing constraints is essential for optimizing network operations and ensuring timely data delivery. In this paper we analysed various synchronization protocols, Algorithm and techniques. Finally, we compared features of TPsN and Rbs protocols, based on accuracy, robustness TPsN is best one. © 2024 IEEE.

关键词： sensor nodes

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Hybrid RF-DNN-LsTM Ensemble for short-Term solar Energy Forecasting

Hybrid RF-DNN-LSTM Ensemble for Short-Term Solar Energy Fore...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Indumathi v v T Ram Pavan Kumar M srilakshmi Ramya s Ch Harika Divya R sahith vellaturi Gopikrishna Department of CSE (AI&ML) B V Raju Institute of Technology Narsapur Medak Telangana India Department of Computer Science Kakaraparti Bhavanarayana College Vijayawada Andhra Pradesh India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of AI&ML Aditya University Surampalem Dept of CSE CVR College of Engineering Hyderabad Dept of IT MLR Institute of Technology Hyderabad

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Given the drastic shift of global sentiment towards renewable energy, it becomes incredibly important to match supply with demand. However the highly variable nature of weather makes it difficult to accurately predict the output of a solar power plant. In this paper, solved this problem by using an ensemble model consisting of both machine learning and neural networks (NN) as base models to forecast the amount of energy that needs to be produced by a solar plant over a short-term time horizon, which in our case will be 0 minute (immediate), 5 minute, 30 minute and 90 minute. Each base model is fine tuned to encourage high diversity and low correlation to improve prediction accuracy. The expected stability or generalization from RF-DNN combined with the memory retention capability of the LsTM network should provide an ideal predictor for time series forecasting of a stochastic process like weather.

关键词： Renewable energy sources Time series analysis Weather forecasting stochastic processes solar energy Predictive models stability analysis Forecasting Long short term memory Random forests

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safeguarding Online Interaction : Utilizing Machine Learning for Detection of Malicious Intent 2

Safeguarding Online Interaction : Utilizing Machine Learning...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Kamali, s. Jeeva, v. bharathKumar, s. Harish, R. Manoj, M. V.S.B. Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350368109

social media platforms are facing a significant challenge with cyberbullying, which is inflicting severe mental health consequences on individuals. To help tackle this, our project is focused on creating computer programs that can identify and prevent cyberbullying on Twitter. We are going to compare three different computer methods: support vector Machine (svM), Naive bayes, and bidirectional Long short-Term Memory (bi-LsTM) models. We will assess the effectiveness of their performance by utilizing key metrics such as accuracy, precision, and recall. Our main goal is to see if these methods can detect cyberbullying early on. Additionally, we will explore the utilization of Natural Language Processing (NLP) to find threats, hate speech, and other harmful words on social media. On the whole, our project wants to highlight how beneficial computer programs can be in stopping cyberbullying and making social media a safer and more welcoming space for everyone. © 2024 IEEE.

关键词： Adversarial machine learning

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An Enhanced Approaches for Interaction Design GIs with virtual Reality 4

An Enhanced Approaches for Interaction Design GIS with Virtu...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： saranya, R. Archana, M. Deebhika sree, P. Akshaya, M. Darsiniga, M.G. V.S.B. Engineering College Computer Science and Engineering Karur India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364729

In this paper, I advance the evolution of three-dimensional Geographic Information systems (GIss) and advocate for their expansion into virtual reality (vR). I commence by scrutinizing existing GIs components pertinent to three-dimensional representation: two-dimensional portrayal, digital elevation modeling, and visualization methods. subsequently, I contend that contemporary advancements in multimedia, three-dimensional modelling, and vR have matured sufficiently to be integrated into GIs frameworks. However, to realize this integration, a genuine three-dimensional GIs necessitates the following functionalities: authentic depiction of the third dimension in data and visualization, unrestricted user mobility within the three-dimensional realm, and compatibility with conventional GIs applications spanning various disciplines such as geography, geology, environmental science, engineering, and computer science. Additionally, remote sensing, employed for purposes like agricultural monitoring, environmental assessment, and urban mapping, must be adapted to operate within the three-dimensional data space, alongside v.s.bility functionalities like line-of-sight assessments. These propositions are exemplified through multiple demonstrations leveraging state-of-the-art high-memory workstations enriched with specialized engineering lexicon. © 2024 IEEE.

关键词： visualization

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Dynamic mobility management scheme for inter handoff in hybrid wireless mesh networks

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International Journal of Mobile Network Design and Innovation 2017年第2期7卷 118-125页

作者： Rao, Appini Narayana Rao, Ch.D.v. subba Department of Computer Science and Engineering JNTU Ananthapur Andhra Pradesh India Department of Computer Science and Engineering College of Engineering S.V. University Tirupati India

Our work focuses to determine the more reliable routing path to increase the quality of service with fast route recovery process and also to find the most effective path for transmitting the same data towards multiple destination nodes. Thus in this paper we have proposed to design a dynamic mobility management scheme for inter handoff in WMN. For mobility, the inter-domain mobility management scheme is applied. To reduce the overall network traffic incurred by this mobility management, the dynamic pointer forwarding scheme for internet traffic is used. simulation results show that the proposed mobility management scheme provides reduced packet drop and delay when compared with the existing scheme. © 2017 Inderscience Enterprises Ltd.

关键词： MEsH networking

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