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Urinary System Diseases Prediction Using Supervised Machine Learning-Based Model: XGBoost and Random Forest 1

引用

International Conference on Artificial Intelligence and Sustainable engineering, AISE 2020

作者： Uttam, Atul Kumar Computer Engineering and Applications GLA University UP Mathura281406 India

ISBN: (数字)9789811685422

ISBN: (纸本)9789811685415

The machine learning model learns from a set of training datasets that have some input features and their respective output in the supervised group. This trained/learned model is then used to forecast the outcome of the collection of test data. The machine-based learning model has recently been widely used in a variety of classification problems in the medical field to diagnose diseases. This study aims to develop a model of machine learning to find out a correct prediction of two urinary-related diseases: acute urinary bladder inflammation and acute renal pelvic nephritis when the characteristics of these diseases are given. The dataset used in this study includes about 120 records of different patients and has six independent variables and one dependent variable (target). In this study, it was observed that supervised model-based machine learning (XGBoost and random forest) can be applied effectively to predict these two urinary diseases. For this classification problem, both models achieved 100 percent accuracy;the result was further validated through k-fold cross-validation, where k = 10. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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A Review on Identification of Unreliability and Fakeness in Social Media Posts using Blockchain Technology 9

A Review on Identification of Unreliability and Fakeness in ...

引用

9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Bajpai, Saumya Tripathi Sharma, Dilip Kumar Gla University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9789380544441

With the rapidly growing news industry and usage of social media, the spread of fake news and solution to interrupt this spread has become a big concern. Due to the digitization, anyone can generate a piece of news and publish it on the internet. The increased number of fake news creates havoc and confuses the reader in a bad way. It is created in such a way that the reader finds it more appealing. Due to the incompetent regulatory services, the news on social media cannot be verified. Therefore, it has become a topic of concern to subdue this business of floating fake news on the internet. This paper aims to conduct a systematic analysis and summarizes the distribution of fake news in real life, its propagation in a decentralized system, how rumor block technique works. Therefore, a total number of 18 relevant papers published on this subject between Jan 2021 and Dec 2022 are selected and thus assimilates the results of works carried out all over the world. The analysis shows that most of the paper used Blockchain technique by fact checking and data centric methodology. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Blockchain

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AI Enabled Convolutional Neural Networks to Detect Brain Tumors 1

引用

3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Rao, Ankur School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

In the early stages of a person’s existence, it is common practice to screen for the presence of brain tumors. Nevertheless, in the current day, it has been improved using various AI computations. Along these same lines, in order to diagnose a patient with a brain tumor, we take into consideration the data of patients, such as MRI scans of the patient’s brain. Our main concern at this point is determining whether or not the tumor is present in the patient’s brain. For a patient to have a healthy lifespan, it is essential for the tumor to be identified at the earliest possible stage. During this phase of the project, we use a calculation called a convolutional neural network to determine the level of severity, which provides accurate results. The division of tumors is an essential and challenging task that falls within the purview of clinical image production. This is due to the fact that human-aided manual characterization might result in erroneous anticipations and discoveries. We proposed the use of a convolutional neural network as a means of differentiating cancers from 2D appealing resonance brain imaging (MRI). The exploratory study was carried out on a dataset that included tumors with a wide range of dimensions, localizations, configurations, and picture forces. Convolutional neural networks, which are built using Keras and Tensorflow, were chosen for usage by our team because, in comparison to regular neural networks, they provide a much-improved presentation. During the course of our investigation, CNN demonstrated an accuracy of 98.73%. Because of its great speculation capacity and quickness, the CNN design that was only recently developed has the potential to be employed as an effective decision-aid device for radiologists working in clinical diagnostics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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Exploring Human Gesture Recognition: Bridging Communication Gaps Through Dynamic Signal Analysis 3rd

Exploring Human Gesture Recognition: Bridging Communication ...

引用

3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (纸本)9789819751457

The ability of humans to communicate more effectively via the use of gestures is widely recognised as one of the most important aspects of human interaction. Because of the inextricable link that exists between body language and successful communication, there has been a significant amount of study conducted in the domains of computer vision and human–computer interaction (HCI). Signal recognition is one of the most important aspects of this study. Signal recognition attempts to analyse and decode the information that is transmitted by human motions. These movements include gestures such as hand and facial expressions. Signal recognition is a key bridge that enables people with varying skills to successfully communicate and connect with others. This is particularly true for those who have trouble understanding speech. There is a possibility that algorithms developed for signal detection can help bridge the communication gap and make interactions more welcoming to all parties involved. These attempts constitute a big step forward in the direction of making interactions with computers seem more natural and more analogous to communication between humans. Static signals and dynamic signals are the two basic types that may be distinguished within the field of signal recognition. These critical frames act as turning points, summing together the most important aspects of the meaning sent by the signal. The purpose of this study is to investigate the area of dynamic signal identification and key frame extraction in order to shed light on the advantages and disadvantages associated with both of these processes. By investigating these facets, our ultimate goal is to get a more profound comprehension of the complexities involved in interpreting dynamic gestures and the possible uses of these gestures. This understanding will add to the continuing attempts to make interactions between humans and computers more natural, inclusive, and human-like, which will eventually improve th

关键词： Gesture recognition

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Machine Learning based selection of Myocardial Complications to Predict Heart Attack 9

Machine Learning based selection of Myocardial Complications...

引用

9th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2022

作者： Saxena, Aditya Kumar, Manish Tyagi, Pawan Sikarwar, Kuldeep Pathak, Aman Department of Computer Engineering & Applications GLA University Mathura Mathura India

ISBN: (数字)9798350332506

ISBN: (纸本)9798350332506

Myocardial Infarction term used for heart attack is a condition in which the functioning of heart becomes abnormal due to various factors like blockage of arteries and veins which in turn results in heart attack. Machine Learning became the most suitable approach to develop the models for prediction in various health care sectors. Apart from myocardial infarction prediction ML is useful in predicting various diseases. Predicting heart disease could be risky if the accurate results are not fetched the patient could die. The analytical tools help in predicting various diseases with the help of existing data of health reports. The motive of this research is to predict heart attack with the help of 12 complications that could possibly happen after the first heart attack. The machine learning algorithms used are Support-Vector Machine (SVM), Logistic-Regression which are used in deploying a model to predict heart attack. This study intends to ideate the prediction for chances of occurrence of a heart attack inside the sufferers. The prime objective of this study is for prediction of the myocardial infarction complications in a patient using UCI machine learning repository and various algorithms. © 2022 IEEE.

关键词： Forecasting

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A Hybrid Methodology for Software Development and IT Team Analysis in Manufacturing 1

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

The IT team working in the manufacturing industry may use this paper to learn about the results of the recently established hybrid software development model with the aid of this paper. The IT team’s needs, difficulties, working practices, past working strategies, and the client’s expectations in the manufacturing firm all play a role in determining how the hybrid model is implemented. The IT team was able to accomplish goals such as on-time delivery with quality improvement, more accuracy, reduced maintenance cycle, the involvement of clients, reduced development time, and improved defect removal efficiency by utilizing a new model and adopting appropriate features from Traditional and Agile methodologies. This article presents a guideline on prior outcomes for the IT team at a manufacturing business and results after using a hybrid approach. The results of deploying a hybrid software development paradigm inside an in-house IT team that works in the manufacturing business are the subject of this research, which explores those results. This study intends to evaluate the effects of combining conventional and agile approaches for software development by thoroughly examining software development practices and determining how successful this combination is. The research investigates the subsequent enhancements in project delivery deadlines, product quality, and team cooperation. The findings provide light on the possible advantages and drawbacks of using a hybrid method. The results give organizations who are looking to optimize their software development processes in a manufacturing setting with useful insights that will eventually contribute to increased efficiency and competitiveness in the sector. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Efficiency

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Utilizing Word Embedding and Linguistic Features for Fake News Detection 9

Utilizing Word Embedding and Linguistic Features for Fake Ne...

引用

9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Sharma, Dilip Kumar Shrivastava, Priya Garg, Sonal Gla University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9789380544441

Fake news detection includes the identification of news consisting of deliberate misinformation propagated by social media or by traditional news media. In this paper, we used features of news text for better news classification. Three categories of linguistic features including writing pattern, quantity, and psycho-linguistic features are considered. These features are combined with news text for final machine-learning classification. Three different feature extraction techniques named Tf-idf, hash-vectorizer, and count-vectorizer are adopted. For experimental analysis of the research, a popular LIAR dataset is used. The proposed method achieved the highest of 72.8% accuracy using a random forest classifier and applying the Tf-idf algorithm. Our proposed method using word-embedding over linguistic features achieved better results in comparison to state-of-art methods. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Decision trees

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Visualization and Prediction of Heart Disease using Big Data Analytics 11

Visualization and Prediction of Heart Disease using Big Data...

引用

11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Shankhdhar, Ashutosh GLA University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9781665487320

Heart disease is the leading cause of deaths worldwide. According to a study published in 2016, one out of every four fatalities in India is caused by cardiovascular disease, which, if not treated properly and immediately, can cause serious health difficulties in patients. Millions of people's lives may be saved if this life-threatening condition could be detected early. This paper proposes a legitimate fitness state detection approach with a heart disease use-case combining machine learning models such as K-Nearest Neighbors, Random Forest algo, and other ensemble learning approaches. This strategy focuses on using machine learning models to predict cardiac problems using real-time data. Stacking CV Classifier with KNN as meta classifier surpasses the other classifiers used in the study with an accuracy rate of 93.39% when using the ensemble learning approach on the data. © 2022 IEEE.

关键词： Data Analytics

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An Ensemble Model for detecting Sarcasm on Social Media 9

An Ensemble Model for detecting Sarcasm on Social Media

引用

9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Sharma, Dilip Kumar Singh, Bhuvanesh Garg, Abhishek Gla University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9789380544441

Nowadays, sarcasm is extensively utilized in regular conversations over social media sites. There have been cases where sarcastic statements have significantly reduced the accuracy in automatically detecting sentiment analysis or fake news. The paper presents natural language processing based text embedding techniques utilizing the ensemble approach in this study. The framework proposes the generation of text embeddings from Word2Vec, Global vectors for word representation (GloVe), and long short-term memory (LSTM) models. Word2Vec and GloVe are word embeddings based generators, while LSTM considers full text. Thus, the proposed framework encapsulates small tweets and sentences scenarios to detect sarcasm. Various natural language processing approaches have been developed, but each has textual context and proximity restrictions. Thus, the proposed framework overcomes this restriction by using word and sentence embeddings and ensembling the output. Two social media datasets, the Headlines dataset and the Twitter dataset, are used to test the proposed model. Headlines datasets have long sentences, while Twitter datasets have short tweets. The accuracy of 81.4% and 88.9% are achieved, respectively. The accuracy metric values are better than previous state-of-art frameworks. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Long short-term memory

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Enhanced-honey bee based load balancing algorithm for cloud environment

Enhanced-honey bee based load balancing algorithm for cloud ...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Singhal, Saurabh Ali, Shabir Shukla, Dhirendra Kumar Dagur, Arvind Papalkar, Rahul Rathod, Vinod Awasthy, Mohan Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India Department of Engineering and Technology Maharastra Navi Mumbai India Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032669663

Cloud computing is an emerging paradigm that brings services to consumers when needed at a lower cost. Adopting cloud computing causes major problems regarding costeffective load balancing. Effective load-balancing solutions in data centers prevent underloaded and overcrowded conditions. When certain services are full of tasks, to ensure Quality of Service (QoS) some of these tasks are transferred to underloaded resources in the same data center. This paper proposes a load-balancing algorithm using an improved honey bee colony algorithm. To balance the load between resources, the algorithm employs the foraging behavior of honey bees. Jobs in overloaded resources are referred to as beehives, while underloaded resources serve as food sources. The approach decreases both the makespan and response time. The experiment was carried out on the CloudSim simulator, and the result shows that makespan was reduced by 11% and response time by 13% when compared with other static and dynamic algorithms. © 2024 The Author(s).

关键词： Cloud computing

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