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2023 International Conference on Innovations in engineering and technology, ICIET 2023

作者： Ashok Kumar, K. Sri Rama Subrahmanyam, K. Srinivasulu Reddy, M. Sathyabama Institute of Scienceand Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309980

The liver is an important organ because it produces vitamins & substances necessary for the survival of the decaying body, including iron, toxins, proteins, & carbs. It aids in vitamin and mineral metabolism, tubular muscle growth, and blood coagulation. Factors including alcohol intake, opioid painkiller usage, poor nutrition, and underlying medical disorders have all been linked to the development of liver disease. The results of a patient's blood tests and physical exam may help physicians determine risk factors for liver disease. Data mining methods may help speed up the process of identifying liver illness. When more information is utilized, the vaccination becomes more precise. This design aims to address this issue by incorporating a method of device mastery into the process of evaluating liver disease. The primary motive of this system is to apply a wide angles set of concepts to perceive healthy folks that are honestly affected by liver disease. The design of this system also aims to evaluate and contrast different bracketing algorithms according to their operational characteristics. A Python-based graphical interface for Stoner is now being development to aid clinicians in the assessment of liver issues. The user interface may be used by soothsayers and medical interpreters as a web application for liver trials. © 2023 IEEE.

关键词： Blood

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Sentiment Analysis for Hateful Content on Social Media

Sentiment Analysis for Hateful Content on Social Media

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Bhattacharjee, Deeshant Paul, Avishek Kumar, Deepak National Institute of Technology Department of Computer Science and Engineering Meghalaya Shillong India

ISBN: (纸本)9798350300826

Hate speech in social media has become a significant concern in today's age, making detecting and analyzing hate speech accurately an area of high importance. Sentiment analysis can aid in automatically detecting hate speech and quantifying its severity, providing valuable insights to social media platforms and law enforcement agencies in taking appropriate action against hate speech. This study aimed to develop and evaluate a deep learning model for hate speech detection and scoring on social media platforms. LSTM and CNN deep learning classifiers were employed and evaluated using standard metrics such as Test Accuracy, Precision, Recall, and F1 Score. The LSTM model performed the best, with 82.67% test accuracy. For regression, seven machine learning models and three deep learning models were tested, with a 3-layer deep learning regressor achieving the best score of 91.28%. The results suggest that deep learning models can effectively detect and categorize hate speech on social media platforms. © 2023 IEEE.

关键词： Sentiment analysis

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Web Application Security Testing Framework using Flask 2

Web Application Security Testing Framework using Flask

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Rohit, R. Hasan Firnas, I. Abishek, M. Albert Mayan, J. Dhamodaran, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665456302

This research presents a novel framework for automated web application security scanning and information gathering using the Axiom methodology. The framework assists organizations and security researchers in identifying and mitigating vulnerabilities in web applications by automating the discovery of publicly available assets and filtering targets based on initial responses, open ports and other criteria. The use of the Axiom methodology allows for faster scanning times and distributed scanning, making it useful for bug bounties and penetration testing. The framework's performance, limitations and challenges are evaluated. The research demonstrates the potential of the framework in improving the efficiency and scalability of web application security while emphasizing the need for proper configuration and ethical considerations. © 2023 IEEE.

关键词： Philosophical aspects

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Transfer Function Based Feature Selection Using Harris Hawk Optimization for Biomedical Applications

Transfer Function Based Feature Selection Using Harris Hawk ...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Jayachitra, S. Prasanth, A. Prasannakiruba, G.S. Abbineni, Srichandana PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India Hyderabad India

ISBN: (纸本)9798331528126

Feature Selection plays key role to truncate the high dimensionality in the datasets through eradicating the unwanted and noisy attributes to improve the performance of classification. The Meta-heuristic technique is one of the most optimistic search techniques to travel the feature space to locate finest feature subset. The Harris Hawk Optimization (HHO) excited from hunting approach of hawk in nature. The cost effectiveness and simplicity formulates the algorithm prominent and superior in solving optimization problems. The binary version of the optimization is accountable for delineating continuous searching space to discrete feature space. Here, 6 transfer functions with S-Shaped and V-Shaped families were interposed and analyzed. Twelve benchmark datasets from UCI repository has been introduced to examine the transfer functions and choose the best feature pertaining to mitigate local minima and high speed of convergence. To analyze the efficacy of transfer function, the proposed method was compared over exiting algorithms and the proposed results attain higher classification accuracy in many of the datasets. © 2024 IEEE.

关键词： Heuristic methods

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Customized Framework for Backend Using Node JS

Customized Framework for Backend Using Node JS

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2023 International Conference on Sustainable Emerging Innovations in engineering and technology, ICSEIET 2023

作者： Dubey, Aman Chauhan, Gaurav Singh Singh, Jatin Girdhar, Palak Bhagwan Parshuram Institute of Technology Department of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350329186

Backend frameworks are software libraries or tools that provide a set of pre-written code to perform common tasks and functions in web development. These frameworks are designed to help developers build server-side applications more efficiently and effectively. Backend frameworks provide a structure and set of conventions that help developers quickly and easily build web applications, increasing development speed. Many backend frameworks are designed with scalability in mind [1], which makes it easier to build web applications that can handle large amounts of traffic and data. Security features like protection against frequent web attacks such as SQL injection and Broken Authentication are often included in backend frameworks[2]. Backend frameworks provide a consistent structure and set of conventions, making it easier to support and update applications of web over time. Since these are generally used, there are a large number of developers who are familiar with the framework, and many resources and tutorials available online, which helps to solve any problems you may encounter while developing the application[3]. Backend frameworks are software libraries or tools that provide a set of pre-written code to perform common tasks and functions in web development. These frameworks are designed to help developers build server-side applications more efficiently and effectively. In summary, backend frameworks are an essential tool for modern web development, as they provide a set of pre-built tools and functionality that can help developers create web applications more efficiently, effectively, and securely. © 2023 IEEE.

关键词： HTTP

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Self-Attention Vision Transformer with Transfer Learning for Efficient Crops and Weeds Classification 6

Self-Attention Vision Transformer with Transfer Learning for...

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

作者： Sharma, Shubham Vardhan, Manu National Institute of Technology Department of Computer Science and Engineering Raipur492010 India

ISBN: (纸本)9798350346961

Classifying and mapping weeds are crucial for ensuring that plantations remain free of unwanted plants, particularly in cases where plant spacing is uneven, making weed detection more challenging than it is for crops. To address this issue, Deep Neural Networks (DNNs) are often used in agriculture to identify the unique features of weeds and classify them based on low-resolution imaging, which helps to control weed populations. Convolutional Neural Networks (CNN) in Deep Learning (DL) models have advanced, achieving remarkable performances in plant-weed classification. Despite Convolutional Neural Networks (CNNs) success in computer vision, they still need help with massive labeled datasets, intra-class classification, and high processing costs when used in classification tasks. Meanwhile, transformer-based models have shown significant modeling capabilities globally but are yet to be thoroughly investigated in agriculture. In this study, we explore Self-Attention Vision Transformers (ViT) using a Transfer Learning (TL) approach to classify weeds in benchmark datasets. The proposed model, ViT-S16, was fine-tuned on the ImageNet-1k dataset, pretrained on the ImageNet-21k, and evaluated on the public benchmark agriculture dataset DeepWeed, which contains plants grown with various weed species under different conditions. Our experimental results show that ViT-S16 outperforms state-of-the-art CNN models ResNeXt-50 and EfficientNet-B7, achieving an utmost accuracy of 98.54%. These results show how helpful ViT can be in different image classification tasks. In the future, researchers can look into the strengths and weaknesses of various ViT models.. © 2023 IEEE.

关键词： Convolutional neural networks

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Brain Disease Classification along with Age Estimation from MRI 4

Brain Disease Classification along with Age Estimation from ...

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4th International Conference for Emerging technology, INCET 2023

作者： Basil Xavier, S. Manoj, Eddula Sai Rohith, Bande Abhilash, K. Velangini Amith Reddy, G. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335750

Deep neural networks can effectively be used to predict the type of brain disease caused to the patient and to estimate the age of the patient. This can be used for identifying the age related disorders. So, we are using MobileNet a machine learning algorithm, and Res Net deep convolutional neural network, by using transfer learning. In transfer learning these models are pre trained on larger data sets. These algorithms have been used to train brain MRI scans that have been divided into three classes [1] Normal, which has not been affected by any disease, [2] Mild Cognitive Impairment, and [3] Alzheimer disease. From the categorized photographs the age of the patient is determined easily. We are going to train our model by using MOBILENET and RESNET algorithms by using the dataset of MRI images along with the accompanying disease *** using this knowledge, the model may identify patterns in the MRI data that point to particular brain illnesses and patient age. The trained model is further used to find the disease type and age of the patients. The outcomes demonstrate that the technique is capable of achieving high accuracy in tasks involving both disease categorization and age estimation. In general, the suggested techniques for categorizing brain disorders and determining age from MRI scans offer a viable answer for enhancing the effectiveness and precision of medical diagnosis and treatment planning for patients with brain diseases. By this model we can predict results in larger scale real time data in short period of time. © 2023 IEEE.

关键词： Magnetic resonance imaging

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SDNS: Artificial Neural Network Scheme to Solve the Nonlinear Skin Disease Model 2

SDNS: Artificial Neural Network Scheme to Solve the Nonlinea...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Ebenezer, V. Sachin, R. Hiruthic, S.S. Sergius, S. Sam Shivnesh, R. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350346640

In the area of medical diagnosis, there has been a need for accurate anomaly detection and disease classification. Data-driven diagnosis is necessary since skin disorders tend to take many different forms, there aren't enough skilled dermatologists, and they're scattered inaccurately. It is important to diagnose and treat skin diseases promptly, but some conditions may not present symptoms right away, which can complicate treatment for certain patients. The patients are also concerned about the treatment and diagnosis. This makes patients a lot harder to reach out to doctors in-order for diagnosis thus an automation is required which gives the results based upon pictures taken personally. © 2023 IEEE.

关键词： Neural networks

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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Resume Classification Using ML Techniques

Resume Classification Using ML Techniques

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2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Surendiran, B. Paturu, Tejus Chirumamilla, Harsha Vardhan Reddy, Maruprolu Naga Raju National Institute of Technology Department of Computer Science and Engineering Puducherry Karaikal India

ISBN: (纸本)9798350312126

In today's world, a typical job ad on the web attracts a massive number of applications in a short period of time. Manual screening of these resumes is not only time-consuming but also very expensive for the hiring companies. To address these challenges, this research paper proposes a solution that aims to automatically classify resumes to their corresponding suitable positions. To find the best possible solution, different ML techniques like Decision Tree, Random Forest, KNN, Support Vector are researched and the most accurate one is chosen. This approach has the potential to revolutionize the hiring process by reducing costs, saving time, and ensuring fairness. © 2023 IEEE.

关键词： Decision trees

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