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Survey on Asclepius: A User-Centric Chatbot Bridging the Gap in Healthcare Integrated with VPN technology

Survey on Asclepius: A User-Centric Chatbot Bridging the Gap...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Garima Shukla Dolly Sharma Vanshaj Awasthi Deepika Shekhawat Pavan Kumar Shukla A. Saranya Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Mumbai Maharashtra India School of Computing Graphic Era Hill University Haldwani India Department of Computer Science and Engineering Amity University Mumbai Mumbai India Department of Electronics and Communication Engineering Noida Institure of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Mumbai India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

This groundbreaking paper introduces Asclepius, a revolutionary software solution aimed at transforming early healthcare assistance through the seamless integration of cutting-edge technologies. At its core lies a sophisticated chatbot system, augmented with machine learning capabilities, facilitating swift and efficient connections between users and local medical professionals. With a strong emphasis on user anonymity and security, particularly concerning sensitive mental health concerns, Asclepius ensures utmost privacy throughout the interaction process. Constructed with meticulous attention to healthcare technology and security, Asclepius has been developed following extensive research across various domains, incorporating insights from acclaimed research articles to optimize and build an effective model. Central to its functionality is the utilization of state-of-the-art facial recognition technology and secure authentication protocols, instilling confidence in the system's reliability by enabling accurate user verification upon login. As of the publishing date, Asclepius has an 89% accuracy for its diagnoses by making use of a Supervised Decision tree-based Machine Learning Model. Through the incorporation of integrated virtual private network (VPN) technology, the project further establishes a secure communication channel between users and healthcare professionals, guaranteeing the protection of sensitive information and fostering a safe, confidential environment for seeking assistance. By seamlessly integrating a vast array of advanced technologies, Asclepius provides users with a comprehensive and user-friendly interface, prioritizing convenience and security in healthcare interactions and heralding a transformative shift in the landscape of early healthcare assistance.

关键词： Privacy Accuracy Face recognition Medical services Machine learning Big Data Chatbots User experience Virtual private networks Medical diagnostic imaging

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Coronary Artery Disease Prediction Using Enhanced Multi Layer DCNN 10

Coronary Artery Disease Prediction Using Enhanced Multi Laye...

引用

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Malik, Varun Sharma, Smita Mittal, Ruchi Rao, A. Kakoli Martin, R. John Khan, Akhilesh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Amity School of Engineering and Technology Amity University Department of Electronics and Communication Engineering Greater Noida India Lloyd Institute of Engineering & Technology Greater Noida India Jazan University Faculty of Computer Science and Information Technology Saudi Arabia Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

Coronary Artery Disease(CAD) is a prevalent and life-threatening cardiovascular condition affecting millions of individuals globally. Early detection and accurate prediction of CAD are vital for timely intervention and prevention of severe cardiac events. Convolutional Neural Networks (CNNs) and other deep learning approaches have recently demonstrated amazing effectiveness in a number of medical applications, including illness prediction. This research proposes an innovative approach, Enhanced Multi-Layer Deep Convolutional Neural Network (EML-DCNN), for the prediction of Coronary Artery Disease. The proposed EML-DCNN architecture integrates advanced deep learning concepts, feature engineering, and data augmentation techniques to enhance the model's predictive performance. The multi-layer structure of EML-DCNN enables the automatic extraction of intricate patterns and features from medical imaging data, specifically focusing on coronary artery images. Additionally, the model incorporates attention mechanisms and residual connections, enhancing its ability to capture subtle details and relationships within the data. To validate the efficacy of the proposed EML-DCNN model, extensive experiments were conducted on a large and diverse dataset comprising coronary artery images obtained from various medical sources. The results demonstrate superior predictive accuracy, sensitivity, and specificity compared to existing CAD prediction models. Moreover, the model's interpretability is enhanced through visualization techniques, providing valuable insights into the features influencing the prediction process. © 2023 IEEE.

关键词： Coronary Artery Disease deep learning disease prediction Enhanced Multi-Layer Deep Convolutional Neural Network

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Enhancing Literary Exploration: A Machine Learning Approach to Book Recommendations

Enhancing Literary Exploration: A Machine Learning Approach ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Kritika Singh Sumita Gupta Saru Dhir Sapna Gambhir Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Noida Uttar Pradesh India Department of Information Technology Amity School of Engineering & Technology Amity University Noida Uttar Pradesh India Department of Computer Science College of Engineering and Computing George Mason University USA

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

In today’s era of rapid internet expansion, the need for efficient information retrieval tools is paramount. Recommendation systems serve as indispensable instruments, aiding users in navigating vast datasets swiftly and accessing relevant information. This paper presents a novel approach to book recommendation systems, offering readers a quick and intuitive method to discover their next read. Implementations encompass three algorithms: Popularity-based, Contentbased, and Collaborative-based recommendation systems. The primary focus lies in proposing a swift book idea system, facilitating users in promptly discovering fitting reading materials. By harnessing the power of these algorithms, readers can explore a diverse range of book options tailored to their preferences, enhancing their overall reading experience and satisfaction. Through this study, we aim to contribute to the advancement of recommendation systems, providing valuable insights and practical solutions for optimizing the book discovery process in the digital age.

关键词： Navigation Instruments Fitting Machine learning Information retrieval Information age Informatics Recommender systems

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Data Security in IoT Networks using Software-Defined Networking: A Review

Data Security in IoT Networks using Software-Defined Network...

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2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022

作者： Shah, Sunil Kumar Sharma, Raghavendra Shukla, Neeraj Amity School of Engineering and Technology Department of Electronics and Communication Engineering Gwalior India Gyan Ganga College of Technology Department of Computer Science and Engineering Jabalpur India

ISBN: (数字)9781665479882

ISBN: (纸本)9781509050017

Wireless Sensor networks can be composed of smart buildings, smart homes, smart grids, and smart mobility, and they can even interconnect all these fields into a large-scale smart city network. Software-Defined Networking is an ideal technology to realize Internet-of-Things (IoT) Network and WSN network requirements and to efficiently enhance the security of these networks. Software defines Networking (SDN) is used to support IoT and WSN related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. This work is a study of different security mechanisms available in SDN for IoT and WSN network secure communication. This work also formulates the problems when existing methods are implemented with different networks parameters. © 2022 IEEE.

关键词： Wireless sensor networks

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A Scalable Solution for Pneumonia Diagnosis: Transfer Learning for Chest X-ray Analysis 7

A Scalable Solution for Pneumonia Diagnosis: Transfer Learni...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Haque, Rezaul Sultana, Shayma Prasad, Chandan Hasan, Sayed Fardin, Hasib Sakib, Abdullah Al Malik, Neeru Kaushik, Keshav East West University Department of Cse Dhaka Bangladesh International American University Department of Management Information System United States Pimpri Chinchwad University Department of Computer Science and Engineering Maharashtra Pune India Westcliff University Department of Engineering Management United States Westcliff University Department of Information Technology United States Pimpri Chinchwad University School of Engineering & Technology Maharashtra Pune India Amity University Amity School of Engineering and Technology Punjab India

ISBN: (纸本)9798350350067

The high death rate and rapid spread of pneumonia make it a serious global health concern. The precision of traditional diagnostic techniques is often compromised, particularly in resource-limited settings. Existing studies have predominantly focused on either binary or multi-class classification, often relying on limited datasets, which affects the generalizability and robustness of models. Additionally, the interpretability and transparency of deep learning models remain challenging in clinical environments. Addressing these challenges is crucial for advancing diagnostic methodologies to combat pneumonia. This study explores the use of transfer learning to improve the reliability of pneumonia classification using chest X-ray images. An automated classification system was developed, utilizing a combination of image preprocessing techniques, feature extraction with GLRLM, and various machine learning and transfer learning algorithms. Two datasets were employed: the GWCMCx dataset with 5856 chest X-rays and another dataset comprising 9207 chest X-rays. The system was designed for both binary and multi-class classification scenarios. CapsuleNet emerged as the most effective model, achieving an accuracy of 99.02% in binary classification and 90.43% in multi-class classification, significantly outperforming previous studies. Additionally, a user-friendly web application was developed to facilitate the integration of the classification system into clinical workflows, accelerating clinical decision-making. This study demonstrates the potential of transfer learning to automate and improve pneumonia diagnosis, offering a scalable solution that can alleviate radiologists' workload and improve patient outcomes. © 2024 IEEE.

关键词： Transfer learning

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Transfer Learning Induced Communication Enhancement for People with varied Disabilities 23

Transfer Learning Induced Communication Enhancement for Peop...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Anupreet Singh Deepak Arora Department of Computer Science & Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India CSE Amity University India

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Cross-Lingual Text Categorization Using Social Media Analysis

Cross-Lingual Text Categorization Using Social Media Analysi...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Biswas, Praloy Neogi, Subhrendu Guha Kulshrestha, Ruchika Saraswat, Sanjeev Ghosh, Malay Chanda, Rishit Amity University Madhya Pradesh Department of Computer Science and Engineering Madhya Pradesh Gwalior India The Neotia University Department of Computer Science and Engineering West Bengal Sarisha India Institute of Business Management GLA University Mathura India University School of Business Chandigarh University Punjab Mohali India SRM Institute of Science and Technology Faculty of Engineering and Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350388534

This article delves into the issues regarding cross-lingual methods for text categorization and sentiment analysis tasks using multilingual social media data. Sentiment analysis ascertains the subjective thoughts or sentiments stated, whereas text categorization automatically applies predefined categories to text. Effective cross-lingual strategies that may transmit knowledge across languages are essential given the popularity of user-generated content on social media platforms that are produced in a variety of languages. We assess the effectiveness of various transfer learning techniques, such as machine translation, cross-lingual word embeddings, and recent developments in multilingual language models, using benchmark datasets spanning multiple language corpus pairs like English-Hindi, English-Telegu) for sentiment analysis and text categorization. Our experiments reveal insights into the strengths and limitations of different cross-lingual transfer approaches. Our results showed the advantages and disadvantages of various cross-lingual transfer strategies. We investigated data augmentation by translation to enhance efficiency for languages with limited resources. The findings have consequences for commercial and research activities in this domain and lead to the development of strong cross-lingual content analysis algorithms for the multilingual social media landscape. © 2024 IEEE.

关键词： Translation (languages)

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An Optimized Forecasting Approach for Virtual Trade Using a Hybrid Arima and Catboost Algorithm

An Optimized Forecasting Approach for Virtual Trade Using a ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Ayush Verma Namrata Dhanda Kapil Kumar Gupta Amity School of Engineering and Technology Amity University Lucknow Uttar Pradesh India Department of Computer Science and Engineering Amity University Lucknow Uttar Pradesh India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The role of Sales forecasting for retail businesses is growing substantially, influencing inventory management practices and resource distribution and consumer demand forecasting. The ARIMA model stands as one of multiple traditional statistical tools used for time-series forecasting because it detects linear patterns together with temporal dependencies. These models face challenges while processing the nonlinear patterns which exist in sales data obtained from real-world scenarios. The CatBoost machine learning model demonstrates strong capabilities in processing nonlinear structures together with different variable types including categories however it cannot detect sequential interdependence. The research presents a new combined forecasting solution which unifies ARIMA and CatBoost so both modeling methodologies work together effectively to boost prediction reliability. The retail sales data analysis utilizes Root Mean Square Error (RMSE) as the performance measurement method. The proposed hybrid model yields an RMSE measure of 103,929.95 which proves better than single CatBoost performance (166,186.48) and standalone ARIMA results (116,704.24). The hybrid model shows a superior performance through its ability to handle features and time dependence which leads to better sales prediction results. Applying the hybrid model to real-world scenarios requires resolution of implementation complexity together with parameter optimization and minimal computational performance issues. The study demonstrates hybrid modeling systems within predictive analytics provide businesses with both extensive scalability and adaptability for situations characterized by uncertainty and dynamic changes. This work also highlights the multitenant hybrid model's flexibility and probability of using predictive analytics in various contexts.

关键词： Analytical models Adaptation models Uncertainty Computational modeling Scalability Time series analysis Predictive models Predictive analytics Root mean square Business

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Fake News Detection Using Boosting Ensemble Method

Fake News Detection Using Boosting Ensemble Method

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International Conference on Confluence The Next Generation Information technology Summit (Confluence)

作者： Kirandeep Kaur Nirbhay Kashyap Computer Science Department Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India

Around the globe, Fake news is a raising concern and has several negative repercussions on people, societies, and countries. It has several detrimental effects on society, including disseminating false information and causing confusion, inciting social unrest and division, eroding credibility and confidence, endangering public health, and causing political instability. To protect people from damage, preserve informational accuracy, stop the spread of hate speech, support a vibrant democracy, and maintain journalism's reputation, fake news detection is crucial. In this work, we attempt to tackle this problem by putting forth a method that uses machine learning classification to distinguish between authentic and fraudulent content. Natural language processing and machine learning ensemble approach are taken into consideration for this. In natural language processing, TF-IDF and Word2vec features are used and optimized using wrapper method (via RFE algorithm). Training and testing are done on “Fake News Detection” dataset from Kaggle. Exploratory examination upon the boosting ensemble method tells that our learners perform better than other options.

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Retinal Image Segmentation based on Machine Learning Techniques 12

Retinal Image Segmentation based on Machine Learning Techniq...

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12th International Conference on Cloud Computing, Data science and engineering, Confluence 2022

作者： Srivastava, Kaushiki Raj Girdhar, Nancy Amity University Amity School Of Engineering And Technology Department Of Computer Science And Engineering Uttar Pradesh Noida India Bennett University School Of Cset Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665437011

Automatic segmentation of medical pictures is a significant stage to extricate valuable data that can assist doctors to detect infections. Eye and systemic infections are known to show themselves in retinal vasculature. Retinal image segmentation is the undertaking of sectioning vessels in retina imagery. Segmentation of retinal vessel is one of the significant strides in retinal image analysis. The order that has been put together for the segmentation of vessel is the chief treatment, cinematography, feature selection, classifier development, testing, education and training in a variety of information and the evaluation of the presentation of the different measures. Many machine learning techniques have already been given by researchers to segment the blood vessels of the images of the retinal fundus of the eyes. A large portion of the accessible retinal vessels division strategies are inclined to more unfortunate outcomes when managing testing circumstances, such as, recognizing low-contrast miniature vessels, vessels with focal reflex, and vessels within the sight of pathologies. We classified the different methods of vascular segmentation of the controlled and uncontrolled practices. Moreover, in this work, we have reviewed and discussed various retinal image segmentation state-of-the-art techniques based on machine learning. © 2022 IEEE.

关键词： Blood vessels

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