PREDICTING RATINGS FOR USER REVIEWS AND OPINION MINING ANALYZE FOR PHYSICIANS AND HOSPITALS

ABSTRACTHealth care is taking its turn in the internet now and online health information consumption is also booming. Users have started generating healthcarereports like online doctor reviews open to all. Hence, online health forums are increasingly popular these days since people can gather their requireddata by just sitting at home and select the best doctor by considering the reviews available online. The patients also browse on their concerneddiseases and use the open forum for discussion on the topics. On an average, these online health-care providers are mainly focusing on reviews aboutthe physicians. The feedback provided by patients is considered and we also analyze the sentiments of the patient to estimate the value of the reviews.The rating for the doctors is divided into various categories such as Staff, Knowledge, and Helpfulness. We propose support vector machine and apriorifor the classification of data and use sentiment based rating prediction to analyze doctor's reviews and opinion mining patterns for online patterns.By providing physician ratings in website, it offers the patients to know about the physician and consider the critique and information to make theirdecision.Keywords: Support vector machine, Apriori, Sentiment classification, Opinion mining

Deep Functional Mapping For Predicting Cancer Outcome

The effective understanding of the biological behavior and prognosis of cancer subtypes is becoming very important in-patient administration. Cancer is a diverse disorder in which a significant medical progression and diagnosis for each subtype can be observed and characterized. Computer-aided diagnosis for early detection and diagnosis of many kinds of diseases has evolved in the last decade. In this research, we address challenges associated with multi-organ disease diagnosis and recommend numerous models for enhanced analysis. We concentrate on evaluating the Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) for brain, lung, and breast scans to detect, segment, and classify types of cancer from biomedical images. Moreover, histopathological, and genomic classification of cancer prognosis has been considered for multi-organ disease diagnosis and biomarker recommendation. We considered multi-modal, multi-class classification during this study. We are proposing implementing deep learning techniques based on Convolutional Neural Network and Generative Adversarial Network. In our proposed research we plan to demonstrate ways to increase the performance of the disease diagnosis by focusing on a combined diagnosis of histology, image processing, and genomics. It has been observed that the combination of medical imaging and gene expression can effectively handle the cancer detection situation with a higher diagnostic rate rather than considering the individual disease diagnosis. This research puts forward a blockchain-based system that facilitates interpretations and enhancements pertaining to automated biomedical systems. In this scheme, a secured sharing of the biomedical images and gene expression has been established. To maintain the secured sharing of the biomedical contents in a distributed system or among the hospitals, a blockchain-based algorithm is considered that generates a secure sequence to identity a hash key. This adaptive feature enables the algorithm to use multiple data types and combines various biomedical images and text records. All data related to patients, including identity, pathological records are encrypted using private key cryptography based on blockchain architecture to maintain data privacy and secure sharing of the biomedical contents

Deep learning-based approach for detecting trajectory modifications of cassini-huygens spacecraft

There were necessary trajectory modifications of Cassini spacecraft during its last 14 years movement cycle of the interplanetary research project. In the scale 1.3 hour of signal propagation time and 1.4-billion-kilometer size of Earth-Cassini channel, complex event detection in the orbit modifications requires special investigation and analysis of the collected big data. The technologies for space exploration warrant a high standard of nuanced and detailed research. The Cassini mission has accumulated quite huge volumes of science records. This generated a curiosity derives mainly from a need to use machine learning to analyze deep space missions. For energy saving considerations, the communication between the Earth and Cassini was executed in non-periodic mode. This paper provides a sophisticated in-depth learning approach for detecting Cassini spacecraft trajectory modifications in post-processing mode. The proposed model utilizes the ability of Long Short Term Memory (LSTM) neural networks for drawing out useful data and learning the time series inner data pattern, along with the forcefulness of LSTM layers for distinguishing dependencies among the long-short term. Our research study exploited the statistical rates, Matthews correlation coefficient, and F1 score to evaluate our models. We carried out multiple tests and evaluated the provided approach against several advanced models. The preparatory analysis showed that exploiting the LSTM layer provides a notable boost in rising the detection process performance. The proposed model achieved a number of 232 trajectory modification detections with 99.98% accuracy among the last 13.35 years of the Cassini spacecraft life. © 2013 IEEE

Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions

Explainability is a crucial requirement for effectiveness as well as the adoption of Machine Learning (ML) models supporting decisions in high-stakes public policy areas such as health, criminal justice, education, and employment, While the field of explainable has expanded in recent years, much of this work has not taken real-world needs into account. A majority of proposed methods use benchmark datasets with generic explainability goals without clear use-cases or intended end-users. As a result, the applicability and effectiveness of this large body of theoretical and methodological work on real-world applications is unclear. This paper focuses on filling this void for the domain of public policy. We develop a taxonomy of explainability use-cases within public policy problems; for each use-case, we define the end-users of explanations and the specific goals explainability has to fulfill; third, we map existing work to these use-cases, identify gaps, and propose research directions to fill those gaps in order to have a practical societal impact through ML.Comment: Submitted for review at Communications of the AC

Investigating Online Financial Misinformation and Its Consequences: A Computational Perspective

The rapid dissemination of information through digital platforms has revolutionized the way we access and consume news and information, particularly in the realm of finance. However, this digital age has also given rise to an alarming proliferation of financial misinformation, which can have detrimental effects on individuals, markets, and the overall economy. This research paper aims to provide a comprehensive survey of online financial misinformation, including its types, sources, and impacts. We first discuss the characteristics and manifestations of financial misinformation, encompassing false claims and misleading content. We explore various case studies that illustrate the detrimental consequences of financial misinformation on the economy. Finally, we highlight the potential impact and implications of detecting financial misinformation. Early detection and mitigation strategies can help protect investors, enhance market transparency, and preserve financial stability. We emphasize the importance of greater awareness, education, and regulation to address the issue of online financial misinformation and safeguard individuals and businesses from its harmful effects. In conclusion, this research paper sheds light on the pervasive issue of online financial misinformation and its wide-ranging consequences. By understanding the types, sources, and impacts of misinformation, stakeholders can work towards implementing effective detection and prevention measures to foster a more informed and resilient financial ecosystem.Comment: 32 pages, 2 figure

Can LLM-Generated Misinformation Be Detected?

The advent of Large Language Models (LLMs) has made a transformative impact. However, the potential that LLMs such as ChatGPT can be exploited to generate misinformation has posed a serious concern to online safety and public trust. A fundamental research question is: will LLM-generated misinformation cause more harm than human-written misinformation? We propose to tackle this question from the perspective of detection difficulty. We first build a taxonomy of LLM-generated misinformation. Then we categorize and validate the potential real-world methods for generating misinformation with LLMs. Then, through extensive empirical investigation, we discover that LLM-generated misinformation can be harder to detect for humans and detectors compared to human-written misinformation with the same semantics, which suggests it can have more deceptive styles and potentially cause more harm. We also discuss the implications of our discovery on combating misinformation in the age of LLMs and the countermeasures.Comment: The code, dataset and more resources on LLMs and misinformation will be released on the project website: https://llm-misinformation.github.io

The Benefits of Dogs

The purpose of this paper is to identify the benefits of dogs. Whether you need a dog for the benefit of having a companion or for physiology benefits. A dog provides love, support, friendship, and assistance to any of our required necessities. A brief discussion about the history of dogs and their responsibilities to the owner will be discussed throughout the paper. A dog is a loyal companion and partner to their owner. They love to aide their benefactor and thrive for love and affection. The next topic section will demonstrate how service dogs can aide in the replacement of the loss of senses and research will demonstrate how a dog can be used to provide a service to resolve the loss of health functions; as well as substantial evidence on how a dog can provide the emotional support and services needed by owners with disabilities. It will continue discussing how dogs have benefited society in health fields, police, military, rescues and during natural disasters. Dogs have saved lives, provided health care needs, performed services for people with disability needs, helped provide for their owner, and have been a care given companion full of love and support

CausaLM: Causal Model Explanation Through Counterfactual Language Models

Understanding predictions made by deep neural networks is notoriously difficult, but also crucial to their dissemination. As all ML-based methods, they are as good as their training data, and can also capture unwanted biases. While there are tools that can help understand whether such biases exist, they do not distinguish between correlation and causation, and might be ill-suited for text-based models and for reasoning about high level language concepts. A key problem of estimating the causal effect of a concept of interest on a given model is that this estimation requires the generation of counterfactual examples, which is challenging with existing generation technology. To bridge that gap, we propose CausaLM, a framework for producing causal model explanations using counterfactual language representation models. Our approach is based on fine-tuning of deep contextualized embedding models with auxiliary adversarial tasks derived from the causal graph of the problem. Concretely, we show that by carefully choosing auxiliary adversarial pre-training tasks, language representation models such as BERT can effectively learn a counterfactual representation for a given concept of interest, and be used to estimate its true causal effect on model performance. A byproduct of our method is a language representation model that is unaffected by the tested concept, which can be useful in mitigating unwanted bias ingrained in the data.Comment: Our code and data are available at: https://amirfeder.github.io/CausaLM/ Under review for the Computational Linguistics journa