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Unveiling the Power of Machine Learning: A Benchmark Analysis of Sentiment Analysis Methods and their Real-World performance 15

Unveiling the Power of Machine Learning: A Benchmark Analysi...

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rokade, Sonali Aitwade, Swapnali Pujari, Siddharaj Kamble, Naresh Computer Science and Engineering Walchand College of Engineering Sangli-Miraj Road Vishrambhag Sangli416415 India KIT Kolhapur Maharashtra State 416 234 India

ISBN: (纸本)9798331300579

the literature review on sentiment analysis provides a thorough exploration of the current state of the field and its wide-ranging applications. Emphasizing the crucial role of machine learning in automating emotion detection from diverse sources like social media, customer feedback, and product reviews, the review delves into existing research, methodologies, and advancements in sentiment analysis. Its primary goal is to synthesize insights, uncover trends, address challenges, and outline future directions. this comprehensive resource is valuable for researchers, practitioners, and decision-makers. through an examination of diverse sources, the review seeks to compile perspectives on strategies employed in data collection, techniques for feature extraction, and the process of model selection. It also emphasizes the importance of handling imbalanced datasets and considering contextual nuances, such as sarcasm, in sentiment analysis. Serving as a foundational resource, this literature review sheds light on the sentiment analysis landscape, highlighting its crucial role in extracting valuable insights from human emotions and opinions encoded in text. the model's robust learning capabilities are evident through its high training accuracy and commendable validation performance. the testing accuracy of 85.9% signifies the model's capacity to generalize successfully to data it has not encountered before. the decreasing training loss signifies efficient convergence during training, contributing to the model's overall robustness. With reasonable processing time per batch, the model allows for efficient testing on new data. Further analysis and fine-tuning could enhance the model's optimization for specific use cases or challenges. © Grenze Scientific Society, 2024.

关键词： Contrastive Learning

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Analysis of the various YOLOv8 models in Detecting Defects in PCB 15

Analysis of the various YOLOv8 models in Detecting Defects i...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Raj, Abhishek Ahlawat, Priyanka National Institute of Technology Department of Computer Engineering Kurukshetra India

ISBN: (纸本)9798350370249

In the arena of electronics, the Printed Circuit Board (PCB) forms the cornerstone of every component, extending from everyday gadgets to advanced defense equipment. the escalating pace of modern life necessitates accelerated PCB production, calling for swift and precise mechanisms to ensure defect-free output for reliable manufacturing. though such quality control tasks were traditionally manual, the advent of machine learning and deep learning has ensured automated and reliable solutions. A standout among these is YOLO, a speedy computer vision algorithm renowned for its unmatched speed and accuracy. this paper seeks to conduct an all-encompassing study of the cutting-edge YOLOv8 sub-models, delving into their individual performance attributes. the work encapsulates the implementation of these YOLOv8 models, followed by a rigorous analysis of their results on a range of metrics like mAP50, mAP50 - 95, precision, and recall. An integral part of this study is the preprocessing of the HRIPCB dataset, comprising six distinct defect categories, namely Missing hole, Mouse bite, Spur, Spurious copper, Open circuit, and Short. the ensuing robust methodology involves comprehensive data preprocessing, data cleansing, and standardization. Ensuring a high-level of data quality, image enhancement techniques such as Colour Balance Adjustment, Histogram Equalization, and Image Filtering - Sharpening are employed. the study shines a spotlight on the individual training and testing processes of the YOLOv8 sub-models, namely v8n, v8s, v8m, v8l, and v8x, leveraging various hyperparameters to optimize model performance. By surpassing the 95.8% mAP50 achieved by earlier YOLO versions, this research aspires to revolutionize PCB defect detection. It paves the way for enhanced quality control mechanisms in PCB manufacturing, thus contributing to the advancement of electronics production regimes © 2024 IEEE.

关键词： Deep Learning Defect detection Image recognition Printed circuit board YOLOv8

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Introduction to Quantum Multi-Agent Reinforcement Learning: Concepts and Applications 15

Introduction to Quantum Multi-Agent Reinforcement Learning: ...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Cho, Yeryeong Bin Son, Seok Park, Soohyun Kim, Joongheon Korea University Department of Electrical and Computer Engineering Seoul Korea Republic of Sookmyung Women's University Division of Computer Science Seoul Korea Republic of

ISBN: (纸本)9798350364637

Advancements in quantum computer hardware and software have paved the way for the application of quantum computing across various fields. With the development of computers with thousands of qubits, the power of quantum is increasing. Because of efficiency in high action dimensions, quantum computing has advantages in multi-agent fields where the amount of data is huge. At the same time, reinforcement learning (RL) has gained prominence for its potential in unknown environments. Consequently, the integration of quantum computing and multi-agent reinforcement learning (MARL), known as quantum multi-agent reinforcement learning (QMARL), is proposed. this paper introduces the overall concept and applications of quantum computing, MARL, and QMARL. It also shows the current technology trends depending on the application. In conclusion, the paper presents challenges and future research directions for QMARL and shows that QMARL can be utilized in various applications for excellent performance. © 2024 IEEE.

关键词： Quantum computers

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HEALth-ML: A Machine Learning Framework for Equity-Driven Public Health Outcome Prediction 15

HEALTH-ML: A Machine Learning Framework for Equity-Driven Pu...

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15th IEEE Annual computing and Communication Workshop and Conference, CCWC 2025

作者： Ekoniak, Jake Asadinia, Marjan University of California Department of Mathematics Los Angeles United States California State University Computer Science Department Northridge United States

ISBN: (纸本)9798331507695

Significant health disparities persist across U.S. counties due to socioeconomic inequalities, environmental challenges, and unequal access to healthcare. Leveraging large health datasets, this study employs machine learning and advanced analytics to classify county health levels-categorized as low, medium, or high-based on environmental, health, and social factors. Emphasizing fairness, we incorporate bias mitigation techniques, ensuring the models avoid reinforcing existing disparities. Our approach includes comprehensive data preprocessing, feature engineering, and employing diverse machine learning algorithms, including Neural Networks, tree-based models, Support Vector Machines, and logistic regression, to capture complex relationships within the data. Results show accuracy scores exceeding 87% and AUC scores over 95%, highlighting the predictive strength of our models. Model performance is evaluated through accuracy, precision, recall, F1-score, and fairness metrics such as Equal Opportunity. Additionally, we assess computational efficiency and interpretability, ensuring the models are both practical and actionable. By providing accurate, fair, and interpretable predictions, this research offers a foundation for data-driven interventions, guiding policymakers toward reducing health inequalities across counties. Our study underscores the ethical imperative of fairness in public health analytics, setting a precedent for prioritizing social equity in future health data research. © 2025 IEEE.

关键词： Electronic health record

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Spectral-Blaze: A high-performance FFT-Based CNN Accelerator 20th

Spectral-Blaze: A High-Performance FFT-Based CNN Accelerator

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20th International symposium on Applied Reconfigurable computing (ARC)

作者： Sunny, Shine Parekkadan Das, Satyajit Indian Inst Technol IIT Palakkad Palakkad Kerala India

ISBN: (纸本)9783031556722;9783031556739

this paper presents Spectral-Blaze, a novel FFT-based CNN accelerator that effectively addresses computational and energy bottlenecks in spatial domain acceleration. the proposed architecture introduces Intra-Patch parallelization during the Hadamard product phase, which optimizes Complex MAC (CMAC) unit utilization and maintains consistent reuse patterns across multiple input feature map patches. this parallelization scheme simplifies the tiling, enabling independent selection of patch elements for efficient on-chip memory storage. Additionally, it leverages the Flex-Stationary dataflow to adaptively store tensors with high reuse opportunities in the on-chip memory, reducing the DRAM traffic. Spectral-Blaze also introduces an optimized FFT/IFFT process that integrates a modified row-column FFT, real FFT-based optimizations, and a vector-radix IFFT. the Spectral-Blaze prototype, implemented on the Zynq MPSoC (ZU7CG), demonstrates impressive performance gains, achieving a speedup of 4.98x for VGG-16 and 1.64x for AlexNet compared to baseline. the Energy-Delay Product (EDP) varies from 4.47x to 25.14x. Compared to flexible dataflow spatial accelerators, Spectral-Blaze achieves an average improvement of 8.82x over Eyeriss and 7.87x improvement over Flexflow in EDP while maintaining a comparable hardware setup. Spectral-Blaze also achieves a 4.59x reduction in MAC operations during convolution, even with VGG-16's compact filter sizes.

关键词： FFT-based convolution Tiling DNN Accelerator Intra-Patch Parallelization Flex-Stationary

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SPADE: Enhancing Adaptive Cyber Deception Strategies with Generative AI and Structured Prompt Engineering 15

SPADE: Enhancing Adaptive Cyber Deception Strategies with Ge...

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15th IEEE Annual computing and Communication Workshop and Conference, CCWC 2025

作者： Ahmed, Shihab Rahman, A. B. M. Mohaimenur Alam, Md Morshed Sajid, Md Sajidul Islam Towson University Department of Computer and Information Sciences TowsonMD United States Unc Charlotte Department of Software and Information Systems CharlotteNC United States School of Cybersecurity Old Dominion University NorfolkVA United States

ISBN: (纸本)9798331507695

the rapid evolution of modern malware presents significant challenges to the development of effective defense mechanisms. Traditional cyber deception techniques often rely on static or manually configured parameters, limiting their adaptability to dynamic and sophisticated threats. this study leverages Generative AI (GenAI) models to automate the creation of adaptive cyber deception ploys, focusing on structured prompt engineering (PE) to enhance relevance, actionability, and deploy-ability. We introduce a systematic framework (SPADE) to address inherent challenges large language models (LLMs) pose to adaptive deceptions, including generalized outputs, ambiguity, under-utilization of contextual information, and scalability constraints. Evaluations across diverse malware scenarios using metrics such as Recall, Exact Match (EM), BLEU Score, and expert quality assessments identified ChatGPT-4o as the top performer. Additionally, it achieved high engagement (93%) and accuracy (96%) with minimal refinements. Gemini and ChatGPT-4o Mini demonstrated competitive performance, with Llama3.2 showing promise despite requiring further optimization. these findings highlight the transformative potential of GenAI in automating scalable, adaptive deception strategies and underscore the critical role of structured PE in advancing real-world cybersecurity applications. © 2025 IEEE.

关键词： Malware

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Automatic aggregation of subtask accesses for nested OpenMP-style tasks 34

Automatic aggregation of subtask accesses for nested OpenMP-...

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34th IEEE International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Shaaban, Omar Aguilar, Jimmy Beltran, Vicenc Carpenter, Paul Ayguade, Eduard Labarta Mancho, Jesus Barcelona Supercomp Ctr Barcelona Spain

ISBN: (数字)9781665451550

ISBN: (纸本)9781665451550

Task-based programming is a high performance and productive model to express parallelism. Tasks encapsulate work to be executed across multiple cores or offloaded to GPUs, FPGAs, other accelerators or other nodes. In order to maintain parallelism and afford maximum freedom to the scheduler, the task dependency graph should be created in parallel and well in advance of task execution. A key limitation with OpenMP and OmpSs-2 tasking is that a task cannot be created until all its accesses and its descendents' accesses are known. Current approaches to work around this limitation either stop task creation and execution using a taskwait or they substitute "fake" accesses known as sentinels. this paper proposes the auto clause, which indicates that the task may create subtasks that access unspecified memory regions or it may allocate and return memory at addresses that are of course not yet known. Unlike approaches using taskwaits, there is no interruption to the concurrent creation and execution of tasks, maintaining parallelism and the scheduler's ability to optimize load balance and data locality. Unlike existing approaches using sentinels, all tasks can be given a precise specification of their own data accesses, so that a single mechanism is used to control task ordering, program data transfers on distributed memory and optimize data locality, e.g. on NUMA systems. the auto clause also provides an incremental path to develop programs with nested tasks, by removing the need for every parent task to have a complete specification of the accesses of its descendent tasks. this is redundant information that can be time consuming and error-prone to describe. We present a straightforward runtime implementation that achieves a 1.4 times speedup for n-body with OmpSs-2@Cluster task offloading to 32 nodes and <4% slowdown for three benchmarks with task offloading to 8 nodes. All code is open source.

关键词： HPC Runtime Systems OpenMP OmpSs-2 Task Parallelism Distributed computing

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BERT in Cognition for Learner 15

BERT in Cognition for Learner

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Ganguly, Souvik Chatterjee, Priyanka Mandal, Kailash Pati School of Computer Science Inspiria Knowledge Campus Siliguri India Dr. B.C. Roy Engineering College Durgapur India National Institute of Technology Durgapur India

ISBN: (纸本)9798350370249

Profound subject knowledge is necessary for education to spark creative or formative projects and boost individual's dynamism. Acquiring a magnificent education is crucial because it permits us to live completely, make wise decisions, and overcome obstacles in life. A learner's academic journey is continued in early education, which also permits them to become more adept at discriminating between inequity and fairness and equips them for a broader spectrum of activities in higher education. Alongside superficial constraints like threats or financial crises, learners must compose a plethora of delightful and demanding activities in addition to their studies. therefore, learners can experience moments of cognitive disorientation and progressively acquire cognitive ailments such as stress or depression, to name a couple. thus, this research suggests a novel Bi-directional Encoder Representations from Transformer (BERT) method for ascertaining learners' cognitive declines. this research utilizes the BERT-Base architecture, and the hyper-tuning of the hyperparameters is carried out by the innovative Tweaked Shuffled Frog Leaping Algorithm technique. Although there is a high need for language models these days, there are currently no specific language models that can reliably specify a learner's cognitive deficits according to psychological inqueries. thus, this work offers a novel strategy known as CogniBERT in response to this. Our approach confounds some of the weaknesses of the BERT methodology by choosing the right scaling factors during training. In terms of training, validation, and testing accuracies, the experimental result surpasses the other approaches, obtaining 99.56%, 98.97%, and 99.29%, respectively, when the suggested method is contrasted with a few state-of-the-art methods. © 2024 IEEE.

关键词： Contrastive Learning

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Proceedings of the International Conference on Cloud computing Technology and Science, CloudCom

Proceedings of the International Conference on Cloud Computi...

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15th IEEE International Conference on Cloud computing Technology and Science, CloudCom 2024

ISBN: (纸本)9798331507589

the proceedings contain 22 papers. the topics discussed include: sampling in cloud benchmarking: a critical review and methodological guidelines;enhancing machine learning performance in dynamic cloud environments with auto-adaptive models;optimal distribution of ML models over edge for applications with high input frequency;Ataru: a lightweight VMM+Runtime for low latency serverless functions;blockchain-based framework for stock market operations: IPOs, trading, and dividend distribution;evaluating fine-tuned BERT-based language models for web API recommendation;enhancing security in EV charging systems: a hybrid detection and mitigation approach;and SW forecaster: an intelligent data-driven approach for water usage demand forecasting.

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Multifaceted Cyberbullying Detection using Deep Learning architectures and Semantic Embeddings in Social Media Discourse 15

Multifaceted Cyberbullying Detection using Deep Learning Arc...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shanmukha Aditya, G. Aggarwal, Ananya Karan, S. Gupta, Deepa Palaniswamy, Suja Venugopalan, Manju Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Mission San Jose High School FremontCA94539 United States

ISBN: (纸本)9798350370249

the Internet is growing into an effective communication tool. But the way the medium is used for targeted communication negatively often referred to as cyberbullying is a matter of concern. To build a model that would be able to identify such text orientations automatically, and block or impose a fine on the user would be great to make the internet a more cordial environment. this study focuses on designing a model for classifying tweets into one of the cyberbullying types and is designed as a six-class classification problem, the classes being: religion, age, ethnicity, gender, noncyberbullying, and others. Before building the model, multiple pre-processing steps are done and then the models are trained. Five different kinds of word embeddings were experimented with, along with five different neural network models. the embeddings used are Keras embedding layer, Glove, W2V, Fast text, and BERT. the neural network models used are BERT, CNN, LSTM, and Bi-LSTM. Multiple combinations of embeddings and neural network models are experimented and the results are discussed. Among the various models scrutinized, the hyperparameter finetuned BERT model distinguishes itself by delivering an outstanding performance, with an accuracy rate of 95\%. this breakthrough suggests that the hyperparameter finetuned BERT model could be instrumental in applications designed to combat the prevalence of cyberbullying and potentially prevent the adverse effects associated with such harmful conduct. CRITICAL: Do Not Use Symbols, Special Characters, Footnotes, or Math in Paper Title or Abstract. © 2024 IEEE.

关键词： Deep learning

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