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Sentiment Analysis and Likes Prediction from Social Media Comments: An ML approach 15

Sentiment Analysis and Likes Prediction from Social Media Co...

引用

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

作者： Sree, M Ramya Siddhartha, Mettu Reddy, Poli Vamsi Vardhan Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The era of social media connectivity has produced a situation where people have platforms to receive approvals from the whole world whereas they used to be restricted to their local community, and in this work, a detailed analysis of the automated analysis of user-generated content on social media platforms with a primary focus on sentiment analysis of the comments on the posts will be presented. In the current era of social media dominated digital arena with people visiting social media for approval and in need of others' advice, it will not work to manually analyze the bulk of conversation. To address this challenge of sentimental analysis, we proposeLinear SVC, Logistic Regression, Gradient Boosting Classifier, Ada Boost Classifier, HistGradient Boosting Classifier with lexical analysis. Out of all HistGradient Boosting Classifier is the best model for sentiment analysis, with the highest accuracy mean of 0.935700 and ROC AUC mean of 0. 9 0 5 6 8 5, indicating superior performance in classification accuracy and class distinction. It is effective to use this method in analysis of the content of live social activity, and this allows to understand the emotions of the people and their engagement with the content, and this leads to the content optimization and the enhancement of the strategies of audience engagement. © 2024 IEEE.

关键词： Sentiment analysis

来源：评论

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Automated Printed Circuit Board Inspection: Incorporating YOLOv8 for Efficient Defect Detection 6

Automated Printed Circuit Board Inspection: Incorporating YO...

引用

6th International Conference on Recent Trends in Advance computing, ICRTAC 2023

作者： Swathi, Gunti Rohan Reddy, B. Ashish Reddy, M. Kumar, R. Prasanna Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai601103 India

ISBN: (纸本)9798350394702

In the realm of modern electronics, Printed Circuit Boards (PCBs) serve as fundamental components, facilitating the seamless integration of circuits across a wide spectrum of applications, from smartphones to space exploration. However, addressing the formidable challenge of efficiently detecting concealed threats within complex environments, where they frequently camouflage themselves within the background, has become a serious threat. To tackle this intricate challenge, the implementation of an Automatic Visual Inspection (AVI) system emerges as an indispensable necessity in today's world. The objective of this research is to create a proficient system for detecting defects in printed circuit boards (PCBs) by employing YOLOv8, the most recent advancement in the YOLO series of object detection models. Our model has achieved a mean Average Precision (mAP50) of 0.955 and (mAP50-95) of 0.516, ensuring the robust detection of PCB defects. Experimental results have revealed that this model exhibits enhanced detection accuracy in comparison to conventional and deep learning object detection models. To assess the performance of this model a comparison with the previous iteration, YOLOv7 with the same experimentation scenarios was conducted. The results demonstrated that the Yolov8 had 17% increment in the mAP50 metric. © 2023 IEEE.

关键词： Printed circuit boards

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CNN-Based Skin Lesion Classification for Melanoma Detection 7th

CNN-Based Skin Lesion Classification for Melanoma Detection

引用

7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Havirbhavi, P. Ashwini, K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Thiruvallur Chennai India

ISBN: (纸本)9783031699818

The significant worldwide health risk posed by skin cancer, especially melanoma, highlights the urgent need for sophisticated diagnostic techniques to facilitate early diagnosis. The goal of this research is to improve the efficiency and accuracy of melanoma diagnosis by doing a thorough analysis of dermatological pictures using CNNs. Two different CNN models are implemented: one uses the ResNet50 architecture for feature extraction, while the other is a specially created CNN for the classification of skin lesions. Thorough preparation of the data, including rotation, flipping, and sophisticated methods like loading, scaling, and normalizing of images, guarantees the best possible model performance. Treating the underlying imbalances in skin lesion datasets is one of the main goals. This is accomplished by computing and adding class weights during training, which is especially important for data that isn’t balanced. Properly calibrated learning rates, accuracy as the evaluation measure, categorical cross-entropy loss, and the Adam optimizer are all used in the model optimization process. One special feature of this project is the incorporation of a progress indicator based on TQDM, which offers real-time information about model convergence when training. Through the careful preparation of data, careful CNN designs, and efficient techniques for handling unbalanced data, this program seeks to substantially enhance the field of skin cancer diagnosis. As the fight against skin cancer never ends, the conclusive goal is to enhance patient outcomes through more precise and early melanoma detection. © IFIP International Federation for Information Processing 2024.

关键词： Oncology

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Heart Stroke Prediction Using Machine Learning and Explainable AI Insights

Heart Stroke Prediction Using Machine Learning and Explainab...

引用

2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Bhanusri, Kodati Reddy, Karukonda Nithin Gnaneswarachary, Paluvayi Nair, Priyanka C. Sampath, Nalini Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350382693

Heart stroke is a critical worldwide medical problem that requires early and accurate prediction in order to provide proper treatment. Recent advancements in machine learning, utilizing models such as XGBoost, Decision Tree, KNN, SVC, and Random Forest, have demonstrated promise in predicting patients at risks from medical data like Age, BMI, Glucose Levels, Hypertension, Heart disease etc., The aim is to evaluate the effectiveness of these machine learning models in predicting people at risk. Metrics like recall, precision, accuracy, and F1-score are examined to assess how well these models perform in predicting the risk of heart strokes. In addition, the research will look at the primary features of medical data that influence model predictions, using Explainable AI techniques such as SHAP. The overall objective is to provide effective and exact approaches for early detection of heart stroke risks, allowing patients to receive timely and important medical care when it is most needed. © 2024 IEEE.

关键词： Prediction models

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SNAVI: A Smart Navigation Assistant for Visually Impaired

SNAVI: A Smart Navigation Assistant for Visually Impaired

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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2022

作者： Seervi, Madhu R Mukhopadhyay, Adwitiya Department of Computer Science Amrita School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus Mysuru India

ISBN: (纸本)9789819946334

Our lives are made easier by automated solutions based on the Internet of Things (IoT). Navigating from one place to another can be challenging for blind people. IoT can increase navigational confidence while simultaneously decreasing reliance on others. The goal is to make the device less bulky and to aid them with two ultrasonic sensors and a flame sensor, which will be cost-effective for blind people who can comfortably travel both indoors and outdoors with minimum sensor use. SNAVI: A Smart Navigation Assistant for the Visually Impaired might be molded into a stick for visually impaired persons so that they receive a notification through their headphones when a barrier is identified in front of them via two ultrasonic sensors, as well as the height of the obstacle. The obstruction might be stationary or moving, and the system can detect fire through a flame IR sensor and send voice alerts. This method helps vision-impaired people travel about with less stress. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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DKWS: A Distributed System for Keyword Search on Massive Graphs (Extended Abstract) 40

DKWS: A Distributed System for Keyword Search on Massive Gra...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Jiang, Jiaxin Choi, Byron Huang, Xin Xu, Jianliang Bhowmick, Sourav S School of Computing National University of Singapore Singapore Hong Kong Baptist University Department of Computer Science Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350317152

Addressing the complexities of querying unstructured graphs such as knowledge graphs and social networks, this paper introduces D KWS, a novel distributed keyword search system. Leveraging a monotonic property, we ensure correct parallelization of our advanced keyword search algorithm, which incorporates tight pruning bounds and is divided into monotonic backward and forward search phases. The system is further augmented by the notify-push paradigm and the PINE programming model, facilitating asynchronous communication and preemptive searches to mitigate staleness in distributed environments. Extensive experiments on real-world datasets demonstrate DKWS's performance advantage, being up to two orders of magnitude faster and incurring 7.6 times lower communication costs than the existing systems. © 2024 IEEE.

关键词： Search engines

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A Hybrid Ensemble Model for Drug Recommendation with Sentiment Analysis from Textual Reviews 15

A Hybrid Ensemble Model for Drug Recommendation with Sentime...

引用

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

作者： Kumar, Nirmal K. Yesudas, Gokul Yoonus, Muhammed Azeem Faheem, V. Rani, Siji S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Kollam Kerala India

ISBN: (纸本)9798350370249

The usage of online recommender systems in healthcare, pharmaceuticals, and hospitals is growing. Nowadays, the vast majority of consumers research potential prescriptions online before consulting their doctors for a variety of medical issues. Modern drug recommendation systems frequently pay no attention to the unique needs of a patient and don’t wait for the expectations of the patient. To this, we propose a novel hybrid ensemble model that incorporates Deep Learning, Machine Learning, and Sentiment Analysis. The contribution of the TensorFlow/Keras neural network is in drug review analysis for sentiment, meanwhile LightGBM classifier that offers an alternative approach is another part of the network. Besides TextBlob and a dictionary-based annotation approach, machine learning taking place during classification brings value to sentiment analysis. Forecasts of authorities are pooled, weighted by review influence, and clashed to produce drug-specific studies. This multi-faceted strategy is better compartmentalized and tailor to each patient’s needs, taking emotional state into account, thereby patients are able to access more accurate drug advice and healthcare professionals to make informed decision. Qualitative analysis of the efficiency the model will be carried out using the confusion matrices. Thus, by means of the research on hand, we expect the transformation of the drug recommendation system taking into consideration and patient-individualized system, which will contribute to the improvement of healthcare delivery. ©2024 IEEE.

关键词： Contrastive Learning

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Automatic time-based learning type analysis towards dynamic personalization 3

Automatic time-based learning type analysis towards dynamic ...

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3rd International Conference on Informatics and computing, ICIC 2018

作者： Laksitowening, Kusuma Ayu Santoso, Harry Budi Hasibuan, Zainal A. Faculty of Computer Science Universitas Indonesia Depok Indonesia School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9781538669204

E-Learning personalization enables system to present learning strategy based on individual learning type. In directing the personalization, the system should identify the learning type of the course participants. The learning type then will become the reference on arranging the appropriate learning strategy. Commonly, the learning type is analyzed once in the beginning of semester. Thus, the learning strategy is given with the assumption that learning type of each learner is constant. In contrast to previous studies, our research considers that learning type may change overtime and learning strategy also need adjustment to the current conditions of the learner. Therefore, the time-based analysis is necessary to acquire the detailed information related to learner's progress over time and learning type changes. This research extracted activity log from e-Learning system to identify learning type of each learner using The Tripe-Factor Approach. Our proposed system integrates automatic scheduled learning type analysis module into existing e-Learning system. Those mechanisms enable system to obtain comprehensive view of learners' behaviour and to readjust the personalized learning strategy according to learning type changes. © 2018 IEEE.

关键词： E-learning

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Automatic Topic Identification in Destination Reviews Using Hybrid Topic Modelling Techniques 15

Automatic Topic Identification in Destination Reviews Using ...

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

作者： Sai, Majji Nitin Royal, Neelam Leeladhar Chakravarthi, Narayana Sai, Nagavarapu Siva Gupta, Deepa Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Nowadays, with user-generated material flooding digital platforms, it is crucial to analyze it thoroughly because it is a huge data source that provides insightful information for both research and economic objectives. Making use of the unstructured destination reviews that are a significant part of this data pool found on the social media platforms, allows for great insight into the preferences of tourists and makes it easier to improve the tourism experience. Using a blend of LDA, LLM, and clustering methods, the proposed model efficiently extracts topics from the vast number of unstructured reviews. For instance the model's efficiency as it undergoes performance review using coherence and silhouette scores, has shown good results, especially in the case of Coorg, a coherence score of 0.48 for LDA. However, the superb performance was revealed in GPT2 by the silhouette score of 0.57 for Coorg. It reflects that the model is quite capable of extracting intrinsic information from complex texts that refer to Coorg. With the use of this method, significant information can be extracted from even the most intricate and contextually rich evaluations, offering players in the tourist industry deep insights, and streamlining their decision-making processes. In the end, this gives stakeholders the ability to efficiently match offerings with visitor preferences. © 2024 IEEE.

关键词： Modeling languages

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A Web-based System for Retrieving Document Images from Digital Library

A Web-based System for Retrieving Document Images from Digit...

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Conference on computer Vision and Pattern Recognition Workshop, CVPRW 2003

作者： Zhang, Li Lu, Yue Tan, Chew Lim Department of Computer Science School of Computing National University of Singapore Kent Ridge Singapore117543 Singapore

ISBN: (纸本)0769519008

A web-based system for retrieving imaged documents from a digital library is described in this paper. First, some image preprocessing is performed off-line on the underlying imaged document to extract its word objects. Then, each word object is represented by a string known as its feature code, based on which a feature code file of the corresponding document is constructed. On the web interface side, the system allows the user to input a set of query words and indicate either to perform "AND" or "OR" operation on them. Once receiving user's request, the system will process each query word and combine the results based on the "AND" or "OR" operation the user has chosen. As for each query word, it is first looked up in an index table that stores words being queried before. If matches are found, results will be retrieved from the index table directly and stored temporarily for subsequent merging. This speeds up searching and makes the system an incremental intelligence system. Otherwise, the system will convert the query word to a feature code string and employ a partial word matching approach to perform search on the pre-generated feature code files. Preliminary experimental results with the imaged documents of students' theses provided by our digital library show that the proposed system is efficient and promising for document image retrieval, and thus has potential applications to digital libraries. © 2003 IEEE.

关键词： Digital libraries

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