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Cutting-Edge Image Recognition Leveraging Deep Learning and Machine Learning for Enhanced Accuracy

Cutting-Edge Image Recognition Leveraging Deep Learning and ...

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Shrivastava, Abhishek Kumar, Vinesh Maurya, Jay Prakash School of Mechanical Engineering VIT Bhopal University Sehore India School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Sehore India School of Computing Science & Engineering VIT Bhopal University Sehore India

ISBN: (纸本)9798331517953

This paper investigates advanced techniques in image recognition and classification by integrating deep learning and machine learning approaches to achieve higher accuracy. Through the implementation of sophisticated training algorithms, the study demonstrates enhanced performance in recognizing and categorizing images across various data models. A major turning point in the development of image identification technology came in 2012 when deep neural networks were introduced. These networks surpassed earlier cutting-edge algorithms and completely changed the computer vision industry. This progress has brought us closer to achieving human-level accuracy in tasks such as identity verification. The role of large datasets like ImageNet is crucial, as they provide the foundation for the success of deep learning. With continuous research pushing the limits of picture identification and producing major advances in human knowledge, deep learning has a huge influence on business, society, and technology. Additional research in this area might lead to creative uses that revolutionize our relationship with our surroundings. Key topics discussed include data pre-processing, post-processing, model optimization, and accuracy enhancement. The findings highlight the potential of cutting-edge technologies to advance image classification and recognition in various sectors, such as medical imaging and visual analysis. The approach emphasizes scalability and adaptability, ensuring that models can be effectively applied to real-world scenarios. Future research will focus on refining these models to handle even more complex image datasets, further enhancing their practical utility and reliability. © 2024 IEEE.

关键词： Convolutional neural networks

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A Transformer-Based Approach for Fake News and Spam Detection in Social Media Using RoBERTa

A Transformer-Based Approach for Fake News and Spam Detectio...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Pal, Ankit Abhijit Mondal, Sudin Kumar, C. Ashok Kumar, C. Jothi School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331509828

The spread of misinformation and spam on social media has become a critical challenge, undermining information integrity and online security. Addressing this pressing issue, this study introduces an advanced solution utilizing RoBERTa, a transformer-based deep learning model, for the effective detection of fake news and spam. By employing sophisticated Natural Language Processing (NLP) techniques, the proposed method demonstrates exceptional accuracy in classifying unreliable content, making it a valuable tool in today's digital communication landscape. The approach is designed to provide real-time detection capabilities, ensuring swift identification and mitigation of misinformation and spam. Its adaptability allows seamless application across different domains, enhancing its versatility. Furthermore, the solution emphasizes sustainability by incorporating continuous monitoring and retraining mechanisms, which enable the model to maintain its performance even as the nature of misinformation evolves. This work underscores the transformative potential of deep learning models like RoBERTa in combating the widespread dissemination of online threats. By ensuring precise, scalable, and timely detection, the study contributes to creating a safer, more trustworthy digital ecosystem. In doing so, it addresses a critical need in the current era, where the reliability of information is paramount for societal and individual well-being. © 2025 IEEE.

关键词： Contrastive Learning

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From Buzz to Blockbuster: Predicting Movie Revenue Using a Hybrid Approach Combining Machine Learning and Sentiment Analysis

From Buzz to Blockbuster: Predicting Movie Revenue Using a H...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Vardhan, Senapathi Vishnu Balaji, Killemsetty Viswa Sai Kumar, C. Ashok Kumar, C. Jothi School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331509828

This research investigates a hybrid approach for predicting movie revenue by integrating machine learning models with sentiment analysis. The growing influence of social media and online discussions offers a valuable opportunity to understand audience engagement and sentiment, which are critical in forecasting box office performance. By analyzing data from various social media platforms alongside movie metadata from sources like IMDb and TMDb, this study seeks to create a comprehensive model that can predict a movie's opening weekend revenue and classify it as a 'Hit,' 'Average,' or 'Flop.' The work highlights the value of audience sentiment, pre-release engagement, and historical box office trends in predicting financial outcomes. By using a combination of quantitative metrics (likes, shares, views) and qualitative data (sentiment scores, discussion themes), the study aims to refine revenue predictions and improve classification accuracy. The hybrid model's ability to incorporate both traditional and real-time data offers several advantages, including better responsiveness to audience perceptions, the ability to identify emerging trends, and more accurate revenue forecasts. This research provides actionable insights that can aid movie studios, marketers, and investors in decision-making processes, ultimately reducing financial risks associated with movie releases. The findings contribute to the growing field of predictive analytics in the entertainment industry, offering a novel and data-driven approach to understanding and forecasting movie success. © 2025 IEEE.

关键词： Predictive analytics

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Hyperbolic Tangent Function-Based Protocols for Global/Semi-Global Finite-Time Consensus of Multi-Agent Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1381-1397页

作者： Zongyu Zuo Jingchuan Tang Ruiqi Ke Qing-Long Han Seventh Research Division Beihang University(BUAA)Beijing 100191China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia IEEE

This paper investigates the problem of global/semi-global finite-time consensus for integrator-type multi-agent *** hyperbolic tangent function-based protocols are pro-posed to achieve global and semi-global finite-time consensus for both single-integrator and double-integrator multi-agent systems with leaderless undirected and leader-following directed commu-nication *** new protocols not only provide an explicit upper-bound estimate for the settling time,but also have a user-prescribed bounded control *** addition,compared to some existing results based on the saturation function,the pro-posed approach considerably simplifies the protocol design and the stability *** examples and an application demonstrate the effectiveness of the proposed protocols.

关键词： Consensus protocol finite-time consensus hyper-bolic tangent function multi-agent systems.

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Accelerating Blockchain-Enabled Federated Learning with Clustered Clients

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IEEE Transactions on Big Data 2024年 1-14页

作者： Cui, Laizhong Li, Yinghao Zhou, Yipeng Qu, Youyang Liu, Jiangchuan College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing FSE Macquarie University Macquare Park NSW Australia Data61 CSIRO VIC Australia School of Computing Science Simon Fraser University Burnaby BC Canada

With the rapid development of big data, Federated learning (FL) has found numerous applications, enabling machine learning (ML) on edge devices while preserving privacy. However, FL still faces crucial challenges, such as single point of failure and poisoning attacks, which motivate the integration of blockchain-enabled FL (BeFL). Beyond that, the efficiency issue still limits the further application of BeFL. To address these issues, we propose a novel decentralized framework: Accelerating Blockchain-Enabled Federated Learning with Clustered Clients (ABFLCC), who utilize actual training time for clustering clients to achieve hierarchical FL and solve the single point of failure problem through blockchain. Additionally, the framework clusters edge devices considering their actual training times, which allows for synchronous FL within clusters and asynchronous FL across clusters simultaneously. This approach guarantees that devices with a similar training time have a consistent global model version, improving the stability of the converging process, while the asynchronous learning between clusters enhances the efficiency of convergence. The proposed framework is evaluated through simulations on three real-world public datasets, demonstrating a training efficiency improvement of 30% to 70% in terms of convergence time compared to existing BeFL systems. IEEE

关键词： Big data

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DSAF: A Dual-Stage Attention Based Multimodal Fusion Framework for Medical Visual Question Answering

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SN Computer science 2025年第4期6卷 1-16页

作者： Mukesh, K. Jayaprakash, S.L. Prasanna Kumar, R. Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamilnadu Chennai 601103 India

In the realm of clinical healthcare, medical visual question answering systems emerge as a pivotal innovation that plays a crucial role in clinical decision-making and patient care. They are designed to interpret medical images in response to free-form natural language questions, thereby facilitating a more nuanced and comprehensive understanding of patient diagnostics. This approach seeks to bridge the gap between complex medical imagery and the clinical questions they prompt, offering a novel avenue for enhancing diagnostic accuracy and efficiency. We introduce the Dual Stage Attention-based Fusion framework, which leverages an advanced feature fusion strategy, along with image encoding techniques and state-of-the-art biomedical language models, to process and analyze medical images and textual queries concurrently. By employing a dual-stage attention mechanism, the proposed model enhances feature extraction and integration, enabling a more accurate and refined analysis of both visual and textual data. The model specifically addresses the challenges commonly associated with medical visual question answering, including the intricate nature of medical imagery and the scarcity of high-quality, annotated medical datasets. The proposed multimodal augmentation technique, involving an autoencoder and generative language model, significantly enriches the data for model training which mitigates the data scarcity issue and enhances the model’s ability to generalize across different medical queries. Empirical validation of our model on the VQA-RAD and SLAKE datasets demonstrates its exceptional performance, achieving state-of-the-art accuracies of 80.8% and 86.2% respectively. These results underscore the model’s potential in AI-enhanced medical diagnostics and decision support systems to offer unprecedented diagnostic insights and patient care solutions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data fusion Dual-stage attention Multimodal learning Vision language model Visual reasoning

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A Hybrid Approach to Dynamic Thread Allocation Driven by Workload and Task Prioritization

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SN Computer science 2025年第3期6卷 1-19页

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

In today’s rapidly changing world, cloud service providers face numerous challenges in managing resources and meeting customer demands. To address these challenges, cloud service providers should prioritize the tasks and allocate threads efficiently. By allocating threads efficiently, service providers can optimize resource utilization and reduce response times and job completion times which in turn improves customer satisfaction. We propose a hybrid model that integrates the threshold-driven thread Balancer (TDTB) for dynamic thread allocation with a Customer Retention Model (CRM) to prioritize tasks based on customer behaviour. Our model initiates the establishment of a thread pool configured with a dynamic threshold based on workload (on-demand requests). Concurrently, a queue to handle incoming customer requests is set up alongside a CRM priority queue designed for task prioritization. Our model calculates Job duration and completion times for n priority classes. Crown consumers experience shorter durations and completion times compared to other priority classes. Tasks are completed in descending order of priority. The results of thorough experimentation clearly show that crown consumer tasks are highly satisfied. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Customer segmentation Response time Service differentiation Thread allocation Throughput Workload

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SPIDER:Speeding up Side-Channel Vulnerability Detection via Test Suite Reduction

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Tsinghua science and Technology 2023年第1期28卷 47-58页

作者： Fei Yan Rushan Wu Liqiang Zhang Yue Cao Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Side-channel attacks allow adversaries to infer sensitive information,such as cryptographic keys or private user data,by monitoring unintentional information leaks of running *** side-channel detection methods can identify numerous potential vulnerabilities in cryptographic implementations with a small amount of execution traces due to the high diffusion of secret inputs in crypto ***,because non-cryptographic programs cover different paths under various sensitive inputs,extending existing tools for identifying information leaks to non-cryptographic applications suffers from either insufficient path coverage or redundant *** address these limitations,we propose a new dynamic analysis framework named SPIDER that uses fuzzing,execution profiling,and clustering for a high path coverage and test suite reduction,and then speeds up the dynamic analysis of side-channel vulnerability detection in non-cryptographic *** analyze eight non-cryptographic programs and ten cryptographic algorithms under SPIDER in a fully automated way,and our results confirm the effectiveness of test suite reduction and the vulnerability detection accuracy of the whole framework.

关键词： side-channel detection test suite reduction dynamic analysis

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Graph Convolutional Networks for SEO: A Comprehensive Framework for Healthcare Information Ranking

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Journal of The Institution of Engineers (India): Series B 2025年 1-16页

作者： RR, Prianka M, Vedaraj R, Anto Arockia Rosaline Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Kavaraipettai Thiruvallur India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India

Internet’s remarkable surge, ubiquitous accessibility, and serviceability have increased users’ dependency on web services for fast search and recovery of wide sources of information. Search engine optimization (SEO) has become paramount in healthcare industries, which helps patients enhance and understand their health status based on their records. In the context of healthcare, it is more significant to improve search results from specific keywords related to clinical conditions, treatments, and healthcare services. So, this research work proposes a Graph Convolutional Network (GCN)-based Search Engine Optimization (SEO) algorithm for healthcare applications. The algorithm utilizes two distinct datasets: MIMIC-III Clinical Database and Consumer Health Search Queries to optimize search engine rankings for health related queries. Following data acquisition, data pre-processing is performed for better enrichment of analysis. The preprocessing steps involve data cleaning, data integration, feature engineering, and knowledge graph construction procedures to remove noisy data, integrate medical data with user search behavior, compute significant features, and construct knowledge graphs, correspondingly. The relation between the data entities is examined within constructed graph through link analysis. The pre-processed data including medical knowledge weights, content relevance scores, and user interaction signals are processed further on GCN model with Adam-tuned weights and bias for ranking healthcare data based on relevance score in response to user query using cosine similarity. The search relevance estimation indicators namely recall, precision, f1-score, and normalized discounted cumulative gain (NDCG) are computed to measure search optimization performance. The proposed GCN-SEO approach benchmarked its effectiveness over existing methods in optimizing web searches related to healthcare with a high performance rate of 95.75% accuracy and 48.25 s dwell time. This sho

关键词： Clinical research

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Stochastic sampled-data multi-objective control of active suspension systems for in-wheel motor driven electric vehicles

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Journal of Automation and Intelligence 2024年第1期3卷 2-18页

作者： Iftikhar Ahmad Xiaohua Ge Qing-Long Han School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise *** focus is placed on the scenario that the dynamical state of the half-vehicle active suspension system is transmitted over an in-vehicle controller area network that only permits the transmission of sampled data *** this purpose,a stochastic sampling mechanism is developed such that the sampling periods can randomly switch among different values with certain mathematical ***,an asynchronous fuzzy sampled-data controller,featuring distinct premise variables from the active suspension system,is constructed to eliminate the stringent requirement that the sampled-data controller has to share the same grades of ***,novel criteria for both stability analysis and controller design are derived in order to guarantee that the resultant closed-loop active suspension system is stochastically stable with simultaneous𝐻2 and𝐻∞performance ***,the effectiveness of the proposed stochastic sampled-data multi-objective control method is verified via several numerical cases studies in both time domain and frequency domain under various road disturbance profiles.

关键词： Active suspension system Electric vehicles In-wheel motor Stochastic sampling Dynamic dampers Sampled-data control Multi-objective control

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