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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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Predictive Analysis of Diabetes Mellitus Using Decision Tree Approach 2

Predictive Analysis of Diabetes Mellitus Using Decision Tree...

引用

2nd Asian Conference on Innovation in Technology, ASIANCON 2022

作者： Thotad, Puneeth Bharamagoudar, Geeta R. Anami, Basavaraj S. Kle Institute of Technology Dept. of Master of Computer Applications Hubballi India Kle Institute of Technology Dept. of Computer Science & Engineering Hubballi India

ISBN: (数字)9781665468510

ISBN: (纸本)9781665468510

Diabetes Mellitus is one of the Non-Communicable Disease. It is characterized by hyperglycemia. It may be reason for numerous health difficulties. According to the statistics in coming years, in 2030, worldwide it reaches up to 643 million counts of diabetic patients. The various factors such as Bad Diet, High Blood Pressure, Obesity, Age, Lack of Exercise etc. can led to Diabetes Mellitus. The various diseases like nerve damage, heart problem, eye problem, kidney disease, stroke in many people caused by high risk of Diabetes Mellitus. Machine Learning plays vital role in healthcare sectors. Using various Machine Learning techniques, a study can be done on large datasets and discover unseen facts, unseen patterns to realize awareness from the data and predict results consequently. In this work, the PIMA Indian Diabetes dataset is considered for Predictive Analysis. The Decision Tree algorithm, namely ID3 (Iterative Dichotomize 3) is used for a predictive analysis. The major phases include preprocessing, feature extraction, classification, and prediction. The classification accuracies of 79.76% and 76.3% are obtained for balanced and unbalanced datasets respectively. The maximum specificity and sensitivity obtained are 79.75% and 79.78% respectively. The ROC curve gives 79.57% overall classification accuracy. The work assists physicians as a second opinion. It can help various health industries to predict the diabetes disease in a better way. © 2022 IEEE.

关键词： Decision trees

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Intelligent System for Detection of Intrusion in Imbalanced Network Traffic 14

Intelligent System for Detection of Intrusion in Imbalanced ...

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Bagla, Piyush Mishra, Amit Kumar Pandey, Neeraj Kumar Sharma, Ravi Dumka, Ankur National Institute of Technology Department of Computer Science & Engineering Jalandhar India Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Department of Computer Science & Engineering Dehradun India Women Institute of Technology Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350344837

Malicious cyberattacks can frequently hide among enormous amounts of regular data in networks with uneven traffic patterns. The identification of imbalance network traffic is difficult to find and making a challenge in terms of Signature building. Despite years of improvement, IDSs still struggle to increase detection accuracy. There are distinct machine learning or deep learning algorithms provides the better results and accuracy for imbalance network traffic. In this study, intrusion detection in unbalanced network traffic is accomplished using both machine learning and deep learning. This process a DSSTE algorithm to avoid imbalance problems. Initially the training set is pre-processed to modify imbalanced data and features are extracted. The proposed model is evaluated with trained data using multiple classification algorithms. © 2024 IEEE.

关键词： Intrusion detection

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An Overview on Breast Cancer Detection & Classification Methods Using Machine Learning Techniques 3

An Overview on Breast Cancer Detection & Classification Meth...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Philip, Rohan Mathew Raj, Rishabh K Shaji, Ashwin Althaf, Fathima Sudarsan, Dhanya Muthoot Institute of Technology and Science Dept. of Computer Science & Engineering Kerala India

ISBN: (纸本)9798350322842

Breast cancer is a prevalent and deadly disease that primarily affects women. Breast cancer has claimed the lives of many people all over the world. Early breast cancer detection, on the other hand, can significantly improve a woman's chances of survival. There are various approaches and procedures for detecting cancer in breast tissue. Breast cancer may be detected using Image Processing, Machine Learning, and Deep Learning methodologies and techniques. People who use trustworthy techniques and make better selections will be able to discover breast cancer in its early stages and save a woman's life. This research aims to contemplate and make inferences from diverse studies on the subject of classification using Machine Learning approaches. The researchers and physicians who devote their time and expertise to enhancing breast cancer screening methods will definitely profit from this research study. © 2023 IEEE.

关键词： Convolutional neural networks

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A Systematic Study of Challenges in Cervical Cytology Image Segmentation of Intelligence based Cervical Cancer Screening 4

A Systematic Study of Challenges in Cervical Cytology Image ...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Anu, P.M. Lakshmi, C. Dept. of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350305258

Cervical Cancer (CC) is the fourth frequent dreadful cancers that prevails in women across the globe. Cervical cytology is being adopted as the standard technique for the screening of pre-cancerous stage and cancerous lesions detection. For accurate segmentation in cervical cytology, the smear images play a crucial role in any Artificial Intelligence based computer Aided Diagnosis (CAD) method. This relies upon the precise segmentation of nucleus present in the cervical cytology smear images. The motive of this study deals with a systematic analysis of the various challenges that are encountered while segmenting the Cervical Cell Nucleus and the countermeasures that are incorporated to eliminate the misdiagnosis and missed diagnoses in the early stage of screening. With the motive of this, a systematic study of the published articles from the sources - Scopus, PubMed and Web of science are analyzed and reviewed. The importance of this study is due to the intensity of the disease that still positions cervical cancer as a bothering disease that disrupts the life of about five lakh female population, every year across the world. © 2023 IEEE.

关键词： Cervical Cancer Cervical Cytology Cervical Nucleus Challenges in Cervical Cytology Segmentation Intelligence based Cervical screening

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Footwear 3D Model Generation from Smartphone 2D Images Utilizing Photogrammetry 3

Footwear 3D Model Generation from Smartphone 2D Images Utili...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Abrar, Raiyan Islam, Md. Tariquel Naim, Mahapara Tabassum, Nuzhat Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350345360

3D modeling in the shoe industry is growing as the technology becomes more widely available. 3D modeling lets companies customize shoes for individual customers, making manufacturing more flexible and efficient. In the shoe industry of Bangladesh, using smartphone images for 3D modeling is a fairly new idea. This allows designers and manufacturers to collaborate and quickly share designs with customers for feedback. A photogrammetry pipeline is a cost-effective and efficient way to create 3D models. This study proposes a potential and affordable method for using photogrammetry to create 3D representations of footwear. The major goal was to determine whether photogrammetry, as opposed to RGB dept. camera photos, can more conveniently construct 3D models of footwear from a small number of smartphone images. In order to assure correct reconstruction, the pipeline required taking a collection of photos from 6-9 distinct perspectives and augmenting the dataset. Then, from these images, point clouds are produced, and from these aligned point clouds, the 3D mesh is produced. The mesh is then textured and smoothed to give color. In Bangladesh's shoe industry, this 3D modeling pipeline can be used for AR shoe try-on. © 2023 IEEE.

关键词： 3D modeling

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A Review of Overfitting Solutions in Smart Depression Detection Models 9

A Review of Overfitting Solutions in Smart Depression Detect...

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

作者： Gupta, Gaurav Kumar Sharma, Dilip Kumar Gla University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9789380544441

Overfitting is a common issue in machine learning-based depression detection model. Overfitting occurs when a machine learning model uses garbage data in the training datasets to the amount that deleteriously impacts on the concert of the model on new data. To predict depression level, most of the model uses machine learning and deep learning approaches. In this paper, a survey is conducted on the solution of overfitting in machine learning-based depression detection models. The survey covers the latest research articles and thesis published in the years 2020 and 2021. The findings of the survey reveal that the early stopping methodology, also called Control the Iteration as is better as compared to the penalty methodology, as it can avoid overfitting problems in the machine learning-based depression detection model. We have listed down other methods also to reduce overfitting like regularization methods, applying ensemble learning, training with more data, cross-validation, etc. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Surveys

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GANs for Synthetic Environmental Data Generation (Air Quality) for Low-Resource Regions 2

GANs for Synthetic Environmental Data Generation (Air Qualit...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Verma, Seema Jain, Neha Mahapatra, Madhumita Setia, Sonia Sachdeva, Vrinda Kumar, Manoj Dept. of Computer Science & Engineering Delhi Technical campus Uttar Pradesh Greater Noida India Dept. of Computer Science & Engineering Sharda University Uttar Pradesh Greater Noida India Dept. of Computer Science & Engineering G.L Bajaj Institute of Technology and Management Uttar Pradesh Greater Noida India Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331541217

Due to the significant expenses and logistical difficulties involved in deploying monitoring equipment, many areas, especially those in developing nations, lack adequate environmental data. This work presents a novel approach that uses Generative Adversarial Networks (GANs) for creating artificial environmental data. The model of GAN is trained for generating synthetic data for under-monitored or unmonitored areas by utilizing high-resolution environmental data from well-monitored regions. The paper discusses the generation of synthetic data and its comparison with real data based on statistical properties. The analysis reveals that the characteristics of the created data closely align with the actual data. With the help of this synthetic data, researchers and policymakers will be able to make more informed decisions by having precise estimations of important environmental indicators like air quality. © 2024 IEEE.

关键词： Generative adversarial networks

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A Novel Framework for Real-Time Fire Detection in CCTV Videos Using a Hybrid Approach of Motion-Flicker Detection, Colour Detection and YOLOv7

A Novel Framework for Real-Time Fire Detection in CCTV Video...

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Jamal, Sangeetha Jackson, Henok Benny, Evin Koshy, Jeroen Dani Joseph, Gloria School of Engineering and Technology Dept. of Computer Science and Engineering Rajagiri Kochi India

ISBN: (纸本)9798350322972

This paper provides a practical view of implementing a real-time vision-based fire detection system that can accurately detect and alert the presence of fires in live CCTV footage. The proposed system combines motion-flicker detection, which uses a background subtraction algorithm to extract the moving objects in a video and a flicker detection algorithm to verify flickering in suspected regions. Then using a YCbCr colour model, the colour of the flickering region is verified to match the characteristics of fire. Further, a YOLOv7 model trained on a large dataset of fire images is employed to verify the results and raises the alarm to alert the presence of fire. Several optimizations have been done to reduce false alarm rate and GPU memory usage times. The accuracy is 98.4%, and false alarm rate is as low as 2.1%. © 2023 IEEE.

关键词： Fires

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A CNN Deep Learning Technique for Botnet Attack Detection for IoT Application 3

A CNN Deep Learning Technique for Botnet Attack Detection fo...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Batham, Yashi Kumar Tiwari, Rakesh Technocrats Institute of Technology and Science Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350322842

The Internet of Things (IoT) is a significant technology that enables us to develop many helpful online applications. The Internet of Things allows remote operating items using preexisting network infrastructure. Services that are based on information technology, computers, and other technologies are included in the cyber world. The cyber-world sees many protocols and technological advancements that lead to improvements. When it comes to cyber-based services, security is an extremely significant issue. Classification methods based on machine learning (ML) and deep learning (DL) can accurately forecast the most effective model for a dataset. A cyber assault on a wide scale carried out by devices infected with malware and managed remotely is known as a botnet attack. Under the context of IoT applications, an effective method for detecting botnet attacks is presented in this research. © 2023 IEEE.

关键词： Internet of things

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