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共 20250 条记录，以下是341-350 订阅

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An Robust Machine learning Methods for Sentiments Analysis of Amazon Product in E-Commerce 3

An Robust Machine Learning Methods for Sentiments Analysis o...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Kumar Mannepalli, Praveen Khatri, Parul Patel, Meenakshi Khan, Sana Chandigarh University Department of Computer Science and Engineering Mohali India Oriental Institute of Science & Technology Bhopal India Bhopal India

ISBN: (纸本)9798331528201

The rise in popularity of online platforms like social media, search engine optimisation, and email has propelled a field of Sentiment Analysis, which seeks to decipher an underlying meaning of a text, to the forefront of global discourse. In order to do sentiment analysis on Amazon product evaluations, this article presents a comparison examination of several machine learning algorithms. SVM, DT, RFC, CNN, and LSTM models will be the primary targets of their investigation. Research using sentiment analysis involves customers who are aware of how a product makes them feel. The data collection utilised is a Kaggle of product reviews from Amazon. It turns out that the LSTM ML algorithm is the most accurate of all the ones that were tried. The results show the LSTM model shows a higher accuracy of 97%, in comparison to other models. Research like this improves both the decision-making process and the online buying experience by shedding light on customer opinion. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

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DemoCraft: Using In-Context learning to Improve Code Generation in Large Language Models 3

DemoCraft: Using In-Context Learning to Improve Code Generat...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Joshua, Kapu Nirmal Sreejith, Mihit Indian Institute of Technology Department of Electrical Engineering Kanpur India Indian Institute of Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331515911

Producing executable code from natural-language directives via Large Language Models (LLMs) involves obstacles like semantic uncertainty and the requirement for task-focused context interpretation. To resolve these difficulties, we introduce DemoCraft, a framework that refines code generation through in-context learning and demonstration selection, in tandem with latent concept learning. Latent concept learning appends extra concept tokens, which act as trainable embeddings encapsulating task-oriented insights. We evaluate our approach on two promi- nent datasets: MBPP and Humaneval. Our experimental findings reveal that this method attains roughly a 2x boost in the pass@k metric compared to baseline models. Moreover, we propose two new evaluation measurements: correctness@k and similarity@k. Empirical analyses indicate that our system achieves nearly a 3x advancement in these measures as well. © 2025 IEEE.

关键词： Semantics

来源：评论

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Brain Region Boundary Recommendation System Based on Deep learning 3

Brain Region Boundary Recommendation System Based on Deep Le...

引用

3rd international conference on Image Processing and Media Computing (ICIPMC)

作者： Liu, Yu Zheng, Yunfa Li, Yuanyuan Wu, Jun Lai, Chuxiao Tian, Hao Hao, Zhilang Qu, Lei Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei Peoples R China China Elect Technol Grp Corp Res Inst 38 Hefei Peoples R China Aero Photogrammetry & Remote Sensing Bur China Co Xian Peoples R China Southeast Univ SEU Allen Joint Ctr Inst Brain & Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350386660;9798350386677

Mapping the brain is a crucial task in brain science research. However, manual brain mapping requires highly specialized skills and consumes significant time and resources to process large-scale data. This paper proposes an automated brain region boundary recommendation system to address the challenge. The system can automatically recommend subsequent monkey brain slices based on the baseline brain map provided by the researcher. The system utilizes deep metric learning to extract features from image patches. This approach can extract richer and more discriminative feature information compared to traditional methods, resulting in improved system recommendation accuracy. The recommendation results demonstrate the system's high accuracy in automated brain mapping and its ability to significantly reduce researchers' workload.

关键词： boundary feature descriptor recommendation

来源：评论

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Smart Oral Health Monitor 3

Smart Oral Health Monitor

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kamkar, Spandana Anilkumar Sanjay, Srushti Vaishnavi, P.S. Karthik, S. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

Oral health is vital to overall well-being but is often overlooked due to inefficient monitoring tools and delayed diagnosis. This project presents a smart handheld device featuring a miniaturized camera for detecting oral issues like plaque, cavities, teeth discoloration and gum diseases in real-time. Using image processing and deep learning algorithms, the device provides precise feedback and actionable insights. Paired with a mobile application offering detailed analysis and recommendations, it empowers users to adopt proactive oral hygiene practices to prevent teeth disorder. © 2025 IEEE.

关键词： Electronic health record

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Real-time Tweets Analysis using Machine learning and Bigdata 3

Real-time Tweets Analysis using Machine Learning and Bigdata

引用

3rd IEEE North Karnataka Subsection Flagship international conference, NKCon 2024

作者： Reddy, P Nandieswar Sai Aswath, S. Alapati, Rithvika Radha, D. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350364569

Twitter's exponential increase in data has transformed it into a rich source for machine learning research, unveiling patterns of opinions and behaviors. This research introduces an automated pipeline built on Kafka, adept at handling large volumes of real-time Twitter data. Utilizing the Twitter API, tweets bearing the hashtag #justice are retrieved and efficiently processed via Kafka. PySpark facilitates data analysis by enabling aggregation, Jaccard similarity computation, and k-means clustering. Preprocessing with Python libraries and TF-IDF enhances analysis accuracy. Jaccard similarity aids in comparing tweets across socio-economic categories. Aggregated data is stored in MongoDB for further examination. Additionally, Streamlit offers visualization tools for effortless exploration of Twitter discourse trends. Thus, this project exemplifies the potential of big data and its impact on investigating social justice discourse in the digital age. © 2024 IEEE.

关键词： Tweets

来源：评论

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Enhancing Dyslexia Detection: A Robust Machine learning Approach 3

Enhancing Dyslexia Detection: A Robust Machine Learning Appr...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Manimaran, V. Shanthi, N. Aravindhraj, N. Rakesh, K. Revanth, K. Saran, S. Department of Computer Science and Engineering Kongu Engineering College Erode India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Dyslexia, recognized as a common learning disability, adversely affects reading, spelling, and word decoding capabilities, frequently resulting in considerable academic and social obstacles if not identified at an early stage. Conventional diagnostic approaches, encompassing neuroimaging and standardized assessments, prove to be effective yet are characterized by high costs and limited scalability. In response to this challenge, the present study introduces a hybrid classification model that integrates Random Forest and XGBoost methodologies to improve dyslexia detection through binary classification. The dataset employed comprises both numerical and categorical variables, such as "Gender" and "Native Language," which were processed via one-hot encoding to facilitate the transformation of categorical information. The Synthetic Minority Oversampling Technique (SMOTE) was utilized to mitigate class imbalance, while Principal Component Analysis (PCA) was implemented to diminish dimensionality, thereby enhancing model performance. Preliminary assessments of individual classifiers indicated that Random Forest surpassed both Decision Tree and Neural Network (MLPClassifier), achieving an accuracy of 95.31%, an F1-score of 0.95, and precision metrics of 0.94 for class 1 and 0.97 for class 0. Expanding upon these findings, a hybrid model that merges Random Forest and XGBoost through the application of a VotingClassifier was constructed. This hybrid framework attained an elevated accuracy of 96.23%, accompanied by consistently robust precision, recall, and F1-scores of 0.96. The results underscore the efficacy of the hybrid methodology in offering a scalable and dependable solution for the early detection of dyslexia within both clinical and educational contexts. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

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Efficient Sentiment Analysis on IMDb Movie Reviews with Synonym Augmentation and Global Average Pooling 3

Efficient Sentiment Analysis on IMDb Movie Reviews with Syno...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Krishna, Aaqil Raj Vardhini, Aluru S Singh, Rimjhim Padam Kanchan, Sneha Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Universiti Tunku AbdulRahman Department of Internet Engineering & Computer Science Malaysia

ISBN: (纸本)9798350372120

With the advancement in the field of automated recommendations, efficient sentiment analysis of reviews has become immensely important. This study presents the application of the Global Average Pooling (GAP) model in efficient sentiment analysis that has traditionally been dominated by models such as Recurrent Neural Networks. Focusing on IMDb movie reviews this paper showcases the effectiveness of the GAP model in handling text data in extracting features and reducing dimensionality. Through this approach, we observed improvements in classification accuracy while using an efficient model. Our findings suggest that the GAP model could serve as an alternative to conventional sentiment analysis techniques. Furthermore, our research indicates benefits from exploring the integration of GAP, with deep learning methods to further enhance performance. © 2024 IEEE.

关键词： Sentiment analysis

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Ensemble learning for Enhanced Mango Leaf Disease Classification: A Deep learning Approach 3

Ensemble Learning for Enhanced Mango Leaf Disease Classifica...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Neog, Protyush P. Kaur, Navjot Department of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798331527549

Mangoes are highly consumed fruits especially in the tropical as well as subtropical zones of the earth. It is said that nearly half of the earth’s population consumes mangoes in some form or the other either as fresh mangoes or in processed form such as juice, dried mangos, or other cooked dishes. That said, this significant food crop has a number of limitations in its cultivation and the least of these is attack by pests. The agro-ecosystem of mango plantation includes several factors such as soil, insects, diseases, weather and many more which determine the well-being of the mango trees in that plantation. Of all these concerns, leaf diseases turn out to be the most offensive by causing losses in both the quantity and quality of mangoes produced. To mitigate these problems, research has been done and continues to be done towards identifying and classifying the mango leaf diseases with better yields and less losses thanks to processing techniques such as deep learning and image processing. Recently published papers focus on these modern methods and analyze the advantages of their applications for the diagnosis and treatment of mango plants. This paper proposes a novel framework that identifies the diseases in mango leaves. The approach demonstrates an impressive 99.94% testing accuracy employing ensemble learning and transfer learning techniques. It is superior to other popular deep learning-based models that include DenseNet121, ResNet50 and VGG19. The results of the study demonstrate the efficiency of the proposed approach as well as its superiority in important performance metrics such as precision and recall. This evolution provides a viable answer to the very pertinent challenge of disease detection in mango cultivation allowing for better management of crops and hence increased production. © 2025 IEEE.

关键词： Plant diseases

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Real-Time Object Detection from Surveillance using Deep learning 3

Real-Time Object Detection from Surveillance using Deep Lear...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Tanisha, A. Tanisha, N. Chaitra, M. Tejasri, P.R. Bnm Institute of Technology Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Object detection in surveillance systems leverages advanced deep learning techniques to enhance security measures through real-time analysis of dynamic video feeds. This project integrates the YOLOv5 model for detecting weapons in both pre-recorded videos and live camera feeds. The model, trained on a dataset with 4000 images labeled for handguns and knives, utilizes image preprocessing steps such as resizing, normalization, and augmentation to improve detection accuracy. Implementing the system with OpenCV and Tkinter, the application processes video streams, identifying potential threats with high precision rate of 0.85 and recall rate of 0.90. The integration of a graphical user interface ensures user-friendly operation for security personnel. Key outcomes demonstrate the efficacy of deep learning models in real-time object detection, significantly contributing to proactive surveillance and enhanced situational awareness. © 2025 IEEE.

关键词： Graphical user interfaces

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A New Federated learning Framework for Plant Leaf Disease Diagnosis 3

A New Federated Learning Framework for Plant Leaf Disease Di...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Singh, Aadarsh Kumar Chattopadhyay, Pratik Singh, Lokesh Pattern Recognition Lab Dept. of Cse Varanasi India Reinventbio.io Varanasi India

ISBN: (纸本)9798331528201

Several Deep learning-based approaches have recently been proposed for plant leaf disease detection. Traditional learning-based models necessitate centralizing large image datasets, requiring huge volumes of storage space. It appears that a better predictive model can be developed if the available data at multiple data sites can be effectively utilized using a collaborative learning strategy. Federated learning (FL) offers a decentralized alternative, enabling collaborative model training by allowing multiple participants to update a global model using their local data without sharing high volumes of raw image datasets. Our work presents an FL framework to train a Vision Transformer-based model with an improved Federated Averaging (FedLossAvg) algorithm. Experimental results demonstrate our model's capability to handle large, varied datasets. Using four clients, our global model achieved an F1-score of 95.62% and an accuracy of 99.89% on the PlantVillage data. © 2024 IEEE.

关键词： Federated learning

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