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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Nazir, Nahida Saraogi, Aditya Kaur, Kanksha Bera, Vinod Singh, Akash Kumar Dahiya, Omdev Singh, Navlesh Kumar Bhardwaj, Kapil Yadav, Suman Avdhesh School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Amity School of Engineering and Technology Department of CSE Amity University Uttar Pradesh India

ISBN: (纸本)9798350304480

One of the main reasons for death worldwide is heart disease, and early detection of the condition can help lower the risk of having a cardiac arrest. This research paper aims to suggest a machine learning-based method for estimating the risk of developing cardiac disease. First recent advancements in the field have been reviewed and then an ML model has been implemented to work on the limitations of the previous research work. To train and evaluate several ML algorithms, Framingham Heart Study (FHS) dataset has been used. The algorithms trained in this research paper are decision trees, random forest, SVM and ANN. It has been demonstrated that ML techniques can significantly improve the accuracy and timeliness of heart disease diagnosis, with SVM achieving the highest accuracy of 86.53%. This research paper provides valuable insights for clinicians, researchers, and data scientists working in the field of cardiovascular disease diagnosis and treatment. © 2023 IEEE.

关键词： ANN Decision Tree Framingham Heart Study Heart Disease Machine Learning Random Forest SVM

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A Systematic Review on Deepfake technology

A Systematic Review on Deepfake Technology

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International Conference on Data Analytics and Management, ICDAM 2022

作者： Khan, Ihtiram Raza Aisha, Saman Kumar, Deepak Mufti, Tabish Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Information Technology Amity University Noida India

ISBN: (纸本)9789811976148

The twenty-first century has seen technology become an integral part of human survival. Living standards have been affected as the technology continues to advance. Deep fake is a deep learning-powered application that recently appeared on the market. It allows to make fake images and videos that people cannot discern from the real ones and is a recent technique that allows swapping two identities within a single video. A wide range of factors, including communities, organizations, security, religions, democratic processes as well as personal lives, are being impacted by deep fakes. In the case of images and videos being presented to an organization as evidence, if the images or videos are altered, the entire truth will be transformed into a lie. It is inevitable that with every new and beneficial technology, some of its adverse effects will follow, causing world problems. There have been several instances in which deep fakes have alarmed the entire network. During the last few years, the number of altered images and videos has grown exponentially, posing a threat to society. Additionally, this paper explores where deep fakes are used, the impacts made by them, as well as the challenges and difficulties associated with deep fakes in this rapidly developing society. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Spatiooral distribution characteristic of covid-19 vaccine using time series forecasting 12

Spatiooral distribution characteristic of covid-19 vaccine u...

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

作者： Talan, Raj Rathee, Sonia Gandhi, Rashmi Msit Computer Science And Engineering Delhi India Amity School Of Engineering And Technology Amity University Computer Science And Engineering Noida India

ISBN: (纸本)9781665437011

Over 170 nations have been affected from Coronavirus disease 2019(COVID-19). In nearly all the afflicted countries, the number of afflicted people and dying people has been rising at a frightening rate. Our biggest option for halting the pandemic's spread is a COVID-19 vaccination. But vaccines are an exhaustible resource. Accurate prediction of vaccine distribution by already implemented policies is critical to assisting policymakers in making sufficient decisions in containing COVID-19 pandemic. Forecasting approaches can be utilized, aiding in the development of better plans and the making of sound judgments. These approaches analyze past events to make more accurate predictions about what will happen in the future according to the current implemented strategy. The effectiveness of various LSTM (Long Short-Term Memory) models as well as the ARIMA (Auto-Regressive Integrated Moving Average) model in projecting vaccine distribution for COVID-19 patients. © 2022 IEEE.

关键词： COVID-19

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RehabPal: Automated Physical Therapy Assistance

RehabPal: Automated Physical Therapy Assistance

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International Conference on Computing and Machine Learning, CML 2024

作者： Arya, P. Sharma, Treya Jatain, Aman Bhaskar, Shalini Bajaj Manju Kumar, Sandeep Department of Computer Science & Technology Amity University Gurgaon India School of Engineering & Technology K R Mangalam University Gurgaon India Department of Computer Science & Engineering and Technology Jaypee Institute of Information Technology Noida India CHRIST University Bengaluru India

ISBN: (纸本)9789819778386

This innovative research project transforms automated physical therapy support by combining 2D and 3D pose assessment approaches with tailored feedback systems. Utilizing Mediapipe technology, the apparatus attentively observes patients’ movements in a two-dimensional space and gives realtime data regarding their gait, exercise compliance, and range of motion. Accurate data on joint angles and body segment alignment are provided by the system's sophisticated 3D posture estimation algorithms, which enhance tracking precision. The system incorporates customized feedback systems that include individual patients’ goals, progress, and conditions and delivery and reward schemes. By increasing user engagement and adherence, the integration of gamification elements has the potential to revolutionize automated physical therapy support. This comprehensive approach aims to enhance patients’ quality of life while simultaneously enhancing long-term rehabilitation outcomes by providing more conveniently accessible, affordably priced, and specially designed therapies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Patient rehabilitation

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Harvest Harmony by Leveraging Machine Learning for Optimal Crop Yield Recommendations 3

Harvest Harmony by Leveraging Machine Learning for Optimal C...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Sahu, Premananda Bharany, Salil Kaushik, Keshav Kaur, Upinder Kumar, Anand Choudhury, Amitava Lovely Professional University School of CSE Punjab Jalandhar India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India Amity School of Engineering and Technology Amity University Punjab Mohali India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara144411 India School of Technology Department of Computer Science and Engineering Pandit Deendayal Energy University Gandhinagar India

ISBN: (纸本)9798350354171

In the past few years, advancement and upgradation in terms of technology has been a massive aspect including the field of data science and machine learning. Taking any one instance out of that aspect is 'CropYieldML systems'. The abstract behind this system is such that it makes up the utilization of complex algorithms to analyze extensive datasets containing information properties of the soil, climatic conditions, and historical crop yields. Based on the data, it's a great assistance for the farmers to make certain decisions on which crop has to be grown upon the desired sort of soil. The focus of this project certainly lies in developing deeply the potential of machine learning techniques in recommending suitable crops based on a variety of environmental and soil parameters. Modeling and Thorough analysis make the project seek to enhance agricultural productivity, profitability, and sustainability. This project can reduce the costs of the inputs and environmental impact by helping the farmers with selective recommendations to optimize their yield by analyzing weather patterns, past crop performances, and soil composition. The data-driven insights can also help the farmers selective decisions for better crops which in the future lead to the sustainability of agriculture. The outcomes of this project are expected to have significant implications for agricultural practices. © 2024 IEEE.

关键词： Adversarial machine learning

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A Hybrid Harmony Search Algorithm based on Firefly Algorithm and Boltzmann Machine

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 241-251页

作者： Yang, J. Zhang, Xiaoxia Fu, S. Hu, Y.Y. Master Student of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China Professor of School of computer Science and Software Engineering University of Science and Technology LiaoNing Anshan11041 China

Harmony Search Algorithm (HS) is a new meta-heuristic algorithm based on music creation process. The algorithm has been widely used in many fields because of its simple and flexible structure and fewer controlled parameters. However, HS is easy to be affected by the initial harmony memory and falls into local optimization. In addition, there are also problems, such as low searching accuracy and slow convergence speed of the optimal solution. Therefore, this paper proposes a hybrid harmony search algorithm based on firefly algorithm and boltzmann machine (HSFA-BM). First, in order to avoid the possibility of harmony search algorithm falling into local optimum, boltzmann machine (BM) mechanism is used to update the harmony memory in this paper. Second, in order to enhance the ability of the algorithm to exploit optimal solutions, this paper uses the movement mode of fireflies to generate new solutions. The improved algorithm enhances the ability of global optimization and search precision, and speeds up the convergence speed. In this paper, six classical test functions are used to test the effectiveness of the algorithm, and a very obvious optimization result is obtained. © 2022. All Rights Reserved.

关键词： Flexible structures

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CE-HigherHRNet: Enhancing Channel Information for Small Persons Bottom-Up Human Pose Estimation

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 260-269页

作者： Li, M.Y. Zhao, J. Master’s Student of School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

Accurately detecting the keypoints of small persons in an image using bottom-up multi-person pose estimation algorithms is exceptionally difficult owing to scale variation challenges. HigherHRNet initially solved the challenge of multi-player scale change pose estimation. However, because it uses repeated cross-scale fusion, owing to inherent defects in channel reduction, semantic information is lost. Furthermore, the aliasing effects produced by the miscellaneous feature maps formed after cross-scale fusion have a significant impact on the detection accuracy of small persons. In this paper, we propose a novel bottom-up human pose estimation algorithm based on HigherHRNet, called Channel-Enhanced HigherHRNet (CE-HigherHRNet). CE-HigherHRNet comprises three main components: a multiscale sub_pixel skip fusion module, a lightweight attention mechanism (with channel attention enhanced and spatial attention modules), and a high-resolution feature pyramid with an added Dupsampling module. The lightweight attention mechanism optimizes the feature map after each fusion. Deconvolution is replaced with Dupsampling, which strengthens the network’s scale awareness and makes it more sensitive to robust scale changes. The average precision (AP) of CE-HigherHRNet on the COCO test-dev dataset was 71.9% (an improvement of 1.4% compared with HigherHRNet). Furthermore, the average detection accuracy of small persons was 68.1% AP (an improvement of 1.5% AP). These results verify that the proposed CE-HigherHRNet is more robust in processing scale changes and has a stronger ability to handle crowded environments. Thus, it is more accurate in positioning small persons in images and human bodies in crowded environments. © 2022. All Rights Reserved.

关键词： Semantics

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IoT-Enabled WBAN and Machine Learning for Emotion Recognition 4

IoT-Enabled WBAN and Machine Learning for Emotion Recognitio...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Mathur, Hritesh Panwar, Harsh Karki, Rahul Jangra, Vivek Yadav, Ramnaresh Kapoor, Anjali Amity University Department of Information Technology Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350387490

All industries, including computer science and health care and customer service are in dire need of efficient recognition of human feelings. This work is a script for a novel approach to affect recognition which involves both vocal emotion analysis and face emotion detection. Voice sentiment analysis project used machine learning technique to identify the prospects of emotional content in voice data. The model also assists in the classification of emotions with reference to the speech emotional states based on added features of MFCC and chroma. On the other hand, face emotion recognition project by applying deep learning algorithms identifies emotions by face expressions. With the help of convolutional, neural networks (CNNs) determining the happiness, sadness, and anger it analyses the facial features correctly. Combining all of these results in a coherent system that allows for the recognition of feelings through voice and or facial signals. Consequently, as demonstrated in the experimental results provided hereinafter, the accuracy of the presented system in correctly identifying human emotions is significantly higher than when using single models on their own. Therefore this combination creates space for other uses such as healthcare marketing among other fields hence improving user experience while at the same time providing unforeseen analysis of human conduct during an interaction with intelligent devices. © 2024 IEEE.

关键词： Convolutional neural networks

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An Approach to Recognize Human Activities based on ConvLSTM and LRCN 6

An Approach to Recognize Human Activities based on ConvLSTM ...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Bhatia, Shradha Chauhan, Tushar Gupta, Sumita Gambhir, Sapna Panchal, Jitesh H. Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Noida India J. C Bose University of Engineering and Technology Department of Computer Engineering Faridabad India Purdue University Department of Mechanical Engineering United States

ISBN: (纸本)9798350346961

In recent times, approaches based on deep learning (DL) have been effectively used to predict a variety of human actions using time series data from smartphones and wearable sensors. Time series data handling remains a barrier for DL-based techniques, even though they did quite well in activity detection. Traditional pattern recognition techniques have achieved significant advancements in recent years. However, the performance of the generalization model may be hampered by the approaches' heavy reliance on human feature extraction. Deep learning methods are becoming more and more successful, and employing these approaches to understand human behaviours in mobile and wearable computing situations or using vision-based technologies has garnered a lot of interest. ConvLSTM and LRCN which is a combination of Convolutional Neural Network (CNN) and Long shirt-term memory (LSTM) are the machine learning methods we employed in this research. With the help of CNNLSTM, it is possible to anticipate human actions more accurately while also simplifying the model and doing away with the necessity for sophisticated feature engineering. Both in terms of space and time, the CNN-LSTM network is deep. In this paper, the LRCN model gets 92% accuracy when we compare the performance of all the models that were utilized against on each other.. © 2023 IEEE.

关键词： Data handling

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Optimized Machine Learning for Classifying Colorectal Tissues

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SN computer science 2023年第5期4卷 1-14页

作者： Tripathi, Ashish Misra, Anuradha Kumar, Kuldeep Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Center for Data Analytics Bond Business School Bond University Gold Coast QLD Australia Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

Due to numerous deaths, colon cancer treatment and diagnosis are viewed as societal and financial challenges. The most severe reason for death worldwide is colorectal cancer. The classification of colon cancer tissues through images is presented in this paper as a multifaceted task. Classifying an illness at a premature stage increases its chances of existence, as late detection can be mortal which results in metastasis and a poor prognosis. The microscopic examination and classification of infected colon tissue sample images is a complex task. Also, the failure to manually detect the abnormality in the tissue by a pathologist might increase the severity of the disease. With the aid of intelligent machines, and automated diagnosis the classification of tissues from images can be done in much less time. These algorithms can learn by analyzing the patterns in the images and support the pathologist in completing the task with greater accuracy. In this research article, we proposed a tuned machine learning model, with the application of five machine learning techniques (K-Nearest Neighbor, Decision Trees, Random Forest, Categorical Boosting, and Gaussian Naive Bayes) for accurately classifying histopathological colon cancer tissues images of National Center for Tumor diseases Bank. The results demonstrate that the Categorical Boosting model has the best performance and is the most viable approach (accuracy: 0.9067, F1-Score: 0.9053, specificity: 0.9739, and sensitivity: 0.9757). © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Colorectal tissue classification Differential-Box-Count Histopathological image classification Machine Learning

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