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Prompt Tuning Models on Sentiment-Aware for Explainable Recommendation 7th

Prompt Tuning Models on Sentiment-Aware for Explainable Reco...

引用

7th international conference on Cognitive computing (ICCC) Held as Part of the Services conference Federation Meeting (SCF)

作者： Long, Xiuhua Jin, Ting Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China

ISBN: (纸本)9783031516702;9783031516719

Explainable recommendation systems primarily utilize users' ratings and corresponding reviews to make recommendations and generate meaningful explanations. However, most explainable recommendation methods only consider one aspect of sentiment in the reviews and lack sufficient exploration of the review texts. Besides, these methods often rely on deep neural networks and do not adequately consider how to effectively integrate extracted information into the pre-trained model to improve the quality of explanation text. In this work, we propose SAPER, which is the first end-to-end framework that combines sentiment analysis and prompt learning in explainable recommendations. For the rating prediction task, we employ multi-granularity sentiment analysis, simultaneously incorporating coarse-grained and fine-grained information from review texts to capture user preferences and item characteristics. For the explanation generation task, we design a multi-level approach that integrates user and item IDs, as well as rating information, as soft prompts into the pre-trained model to generate personalized explanations. Experimental results demonstrate that our model outperforms state-of-the-art baseline models on three datasets. In particular, we validate the effectiveness of multi-granularity sentiment analysis and soft prompt strategies. Experimental analysis indicates that the recommendation explanations generated by the SAPER model are semantically closer to actual reviews.

关键词： Explainable Recommendation Sentiment Analysis soft Prompts Pre-trained Models Joint learning

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House Price Prediction using machine learning Algorithm 7

House Price Prediction using Machine Learning Algorithm

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Sharma, Shailendra Arora, Deepti Shankar, Gori Sharma, Priyanka Motwani, Vihaan Department of ECE Shree Digambar Institute of Technology Rajasthan Dausa India Department of Mathematics Arya Institute of Engineering & Technology Rajasthan Jaipur India Department of ECE Jaipur Engineering College Rajasthan Jaipur India Department of Mathematics Jaipur Engineering College Rajasthan Jaipur India Shiv Nadar School Gurgaon India

ISBN: (纸本)9781665464086

Houses are one of the fundamental needs in human society. Good and peaceful surroundings where people feel comfortable are called houses. Hence, to have a good and happy living, people need to choose a good house model. this article focuses on accurately predicting house prices by using machine learning algorithms. this model helps people in selecting the house that is suitable for their living. the main parameter people look for will be the surrounding area, house type, price, location, and some other amenities. All these factors are consideredto find out the required house for living. As people are very concerned about their budgets for buying a house, the prediction of house prices should be very precise. It also helps people in choosing houses based on their budgets, which do not affect their financial state in the future. the main outcome of this model is to predict the price of a house accurately as per the user requirements. this study has attempted to implement various machine learning algorithms like Linear Regression (LR), Gradient Boosting Regressor (GBR), Histogram Gradient Boosting Regressor, and Random Forest (RF) Regressor algorithms. Finally, the algorithm that generates high accuracy is considered for predicting the house price. © 2023 IEEE.

关键词： Houses

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Medical Diagnosis of Human Heart Diseases with and without Hyperparameter tuning through machine learning 7

Medical Diagnosis of Human Heart Diseases with and without H...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Baseer, K.K. Nas, S.B. Asqeem Dharani, S. Sravani, S. Yashwanth, P. Jyothirmai, P. Tirupati India Dept. of Information Technology Sree Vidyanikethan Engg. College Tirupati India

ISBN: (纸本)9781665464086

In medical field, cardiac disease prediction remains as a complex task. Heart disease is the major cause of death around the world. this study has surveyed various research papers and understood the methodologies used by various authors, their requirements and the challenges faced in the implementation stage. the exisiting research work on heart disease prediction uses machine learning models like Logistic Regression (LR), Random Forest (RF), Support Vector machine (SVM), K-nearest neighbor (KNN) to get the accurate results. the present study focuses on hyperparameter tuning. Hyperparameters vary from each model and the best parameters will also change according to the dataset. By using the best parameters in learning model can give the prediction results in less time. Tuning of best hyperparameters can be done by using GridSearchCV. this study develops a web application by obtaining the input from the user. Features of heart diseases are retrieved from the historical data (the heart dataset). the major challenge is that the models mostly fails to give accurate results. Sometimes Hyperparameter tuning will give accurate result and in some cases it won't. It depends upon the type of model selected for model building. the proposed system mainly concentrates on comparision of models with and without Hyperparameter tuning basedon the accuracy. © 2023 IEEE.

关键词： Support vector machines

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Developing a Pre-Consultation System using machine learning for Medical Diagnostics 7

Developing a Pre-Consultation System using Machine Learning ...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Likhitha, Medisetty Kalyani, G. Vennela, Tulluri Naga Paul, Dharmapuri Mahith Department of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India

ISBN: (纸本)9781665464086

Due to their lifestyle choices and environment, people nowadays suffer from a broad variety of ailments. therefore, it becomes crucial to explore the potential for earlier disease prediction. However, doctors find it impossible to accurately forecast based solely on symptoms. the most difficult challenge is making an accurate disease forecast. machine learning is crucial in disease prediction to address this problem. In the area of medicine, the re is a huge data increase each year. As a result of the expansion of data in the medical and healthcare sectors, reliable analysis of medical data-which benefited from early patient care -has grown. machine learning algorithms can discover possibilities in the provided medical dataset with the use of disease data. the main objective of this study is detecting the probable disease with high accuracy and challenges are data quality & quantity and high dimensionality. this study has suggested a broad possibility of disease occurrence based on the symptoms of the patient. Decision Tree, Random Forest, K-Nearest Neighbor (KNN), and Naive Bayes algorithms are used to accurately forecast diseases. A collection of disease symptoms is necessary for disease *** accuracy of the prediction is the main challenge which depends on the quality and quantity of the data used to train the model. Medical data can be incomplete, biased, or inconsistent, which can affect the performance of the model. So our goal is to develop the correct accuracy prediction model. © 2023 IEEE.

关键词： Forecasting

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Human-Centered Explainable AI at the Edge for eHealth 7

Human-Centered Explainable AI at the Edge for eHealth

引用

7th IEEE international conference on Edge computing and Communications (IEEE EDGE) / IEEE World Congress on Services (SERVICES)

作者： Dutta, Joy Puthal, Deepak Khalifa Univ C2PS Abu Dhabi U Arab Emirates Khalifa Univ Dept EECS Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350304831

Explainable Artificial Intelligence (XAI) is a new paradigm of Artificial Intelligence (AI) that is giving different AI/machine learning (ML) models a boost to penetrate sectors where people are thinking about adopting AI. this work focuses on the adoption of XAI in the health sector. It portrays that careful integration of XAI in both cloud and edge could change the whole healthcare industry and make humans more aware of their present health conditions, which is the need of the hour. To demonstrate the same, we have done an experiment based on the prediction of a particular medical condition called "cardiac arrest" in a specific subject group (patients who are 70 years old). Here, based on the explanation provided by the XAI model (e.g., SHAP, LIME) at Cloud and Edge, our system can predict the chances of a "cardiac arrest" for the subject with a valid explanation. this type of model will be the next big upgrade in the healthcare industry in terms of automation and a self-explanatory system that works as a personal health assistant for individuals.

关键词： XAI IoMT Edge machine learning Interpretability eHealth

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Offload Shaping for Wearable Cognitive Assistance 7

Offload Shaping for Wearable Cognitive Assistance

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7th IEEE international conference on Edge computing and Communications (IEEE EDGE) / IEEE World Congress on Services (SERVICES)

作者： Iyengar, Roger Dong, Qifei Chanh Nguyen Pillai, Padmanabhan Satyanarayanan, Mahadev Carnegie Mellon Univ Pittsburgh PA 15213 USA Intel Labs Pittsburgh PA USA

ISBN: (纸本)9798350304831

Edge computing has much lower elasticity than cloud computing because cloudlets have much smaller physical and electrical footprints than a data center. this hurts the scalability of applications that involve low-latency edge offload. We show how this problem can be addressed by leveraging the growing sophistication and compute capability of recent wearable devices. We investigate four Wearable Cognitive Assistance applications on three wearable devices, and show that the technique of offload shaping can significantly reduce network utilization and cloudlet load without compromising accuracy or performance.

关键词： Computer Vision machine learning Offloading Wearable computing Mobile computing Edge computing IoT Cloudlet Augmented Reality 5G Wi-Fi

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Multiple Disease Prediction by Applying machine learning and Deep learning Algorithms 7

Multiple Disease Prediction by Applying Machine Learning and...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Chowdary, M. Kalpana Kumar, K.Anil Ganesh, C. Turaka, Rajsekhar Rao, B.Devananda Naik, Sk.Lokesh MLR Institute of Technology Department of CSE Hyderabad India Malla Reddy Engineering College Department of ECE Hyderabad India Sri Eshwar College of Engineering Department of CCE Coimbatore India Nalla Narsimha Reddy Education Soviety's Group of Institutions Department of ECE Hyderabad India

ISBN: (纸本)9798350397253

A large number of machine-learning projects for healthcare inspection concentrate on just one disease at one time. One analysis is performed for each of the following conditions: diabetes, cancer, skin disease, and heart disease. A single analysis cannot forecast more than one disease using a same system. Numerous machine learning approaches can perform predictive analytics on colossal data sets. Although using predictive modeling in healthcare is challenging, it will eventually help practitioners make quick choices about the health and treatment of patients based on vast amounts of data. In this post, a system that uses the Flask API to forecast numerous diseases is proposed. Tensor flow, Flask API, and machine learning techniques were used to implement multiple illness analyses. the model behavior is saved using Python pickling, and the pickle file is loaded when needed using Python unpickling. the goal in this effort is to create a web application that can forecast numerous diseases, such as breast cancer, diabetes, heart disease, malaria, and pneumonia, using the principles of machine learning and deep learning. the finished model's behavior will be captured in a Python pickle file. the Flask API is created. the parameters of the disease must be sent together with the disease name when using this API. the relevant model will be called by the Flask API, which then returns the patient's state. In order to track patient condition and provide early warnings to patients in order to reduce death rates, it is important to identify the most common diseases. © 2023 IEEE.

关键词： Python

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Speech Recognition and Neural Networks based Talking Health Care Bot (thCB): Medibot 7

Speech Recognition and Neural Networks based Talking Health ...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Bandopadhyay, Dwaipayan Ghosh, Rajdeep Chatterjee, Rajdeep Das, Nabanita Sadhukhan, Bikash Department of CSE Techno International New Town Kolkata India

ISBN: (纸本)9781665464086

the COVID-19 pandemic has affected healthcare in several ways. Some patients were unable to make it to appointments due to curfews, transportation restrictions, and stay-at-home directives, while less urgent procedures were postponed or cancelled. Others steered clear of hospitals out of fear of contracting an infection. With the use of a conversational artificial intelligence-based program, the Talking Health Care Bot (thCB) could be useful during the pandemic by allowing patients to receive supportive care without physically visiting a hospital. therefore, the thCB will drastically and quickly change in-person care to patient consultation through the internet. To give patients free primary healthcare and to narrow the supply-demand gap for human healthcare professionals, this work created a conversational bot based on artificial intelligence and machine learning. the study proposes a revolutionary computer program that serves as a patient's personal virtual doctor. the program was carefully created and thoroughly trained to communicate with patients as if they were real people. Based on a serverless architecture, this application predicts the disease based on the symptoms of the patients. A Talking Healthcare chatbot confronts several challenges, but the user's accent is by far the most challenging. this study has then evaluated the proposed model by using one hundred different voices and symptoms, achieving an accuracy rate of 77%. © 2023 IEEE.

关键词： machine learning

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A Comprehensive Review of Predicting Lifestyle-Based Disease Specifically PCOS Among Women Using Data Mining and machine learning Approaches 7th

A Comprehensive Review of Predicting Lifestyle-Based Disease...

引用

7th international conference on soft computing: theories and Applications, SoCTA 2023

作者： Gupta, Vaishali Suresh, P.V. SOCIS New Delhi India IGNOU SOCIS New Delhi India

ISBN: (纸本)9789819720880

Urbanization and industrialization are increasing risk factors associated with lifestyle choices, contributing to the advancement in lifestyle-related diseases. this primarily results from habits that encourage a sedentary lifestyle, leading to various health issues and ultimately peaking in chronic Non-Communicable Diseases (NCDs). Numerous lifestyle-based diseases affecting women globally include cardiovascular disease, breast cancer, PCOS, type-2 diabetes, thyroid disorders, and more. PCOS, or polycystic ovary syndrome, stands out as a prevalent health concern affecting a significant proportion of women worldwide. the timely identification and accurate prediction of PCOS are crucial. Numerous researchers have dedicated their efforts to predicting PCOS using data mining and machine learning approaches. this Comprehensive Review (CR) seeks to thoroughly examine and summarize various disease prediction models proposed in current research for predicting PCOS. the paper scrutinizes technical aspects such as data collection selection, feature engineering applications, data preprocessing, characteristics of data mining and machine learning techniques, methodology, and evaluation matrices. However, none of the selected studies conducted external validation. the paper also explores the limitations and outlines the scope for future research perspectives in predicting PCOS, presenting opportunities for scholars interested in exploring additional methods to anticipate early-stage risks associated with PCOS. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Health risks

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Monitoring and Analysis of Students' Live Behaviour using machine learning 7

Monitoring and Analysis of Students' Live Behaviour using Ma...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Akash, S. Dinesh, V. Muthamil Selvan, S. Alfred Daniel, J. Srikanth, R. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397253

the abrupt shift in world events has badly harmed many sectors. One of the most important areas of the educational industry is where many educational institutions have selected online learning platforms. It has expanded the distance between teachers and students, the recent polls on student academic engagement, indicates that students are more engaged in classroom instruction than in studying. Even though most teachers are conversant with online educational technology, student and teacher participation in online classrooms has been lacking. In this scenario, the tool is valuable for determining how the student has been listening to the entire class and for assessing the student's conduct and delivering a report based on it. the primary objective of this article is to develop a self-sufficient agent capable of supplying instructors and students with knowledge. the level of student engagement is substantially correlated with critical thinking and subject-specific grades. this study includes the addition of zoom to the current virtual meeting platform. © 2023 IEEE.

关键词： Face recognition

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