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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Jabez, J. Franklin, Ramya G Ronald Doni, A. Kamalesh, Murari Devakannan Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

A key a part of early analysis and treatment of neurological illnesses is the detection of anomalies inside the mind through ultrasound scans. Using a Signal Processing Network (SPN), this studies seems into the cost of characteristic extraction techniques for identifying and categorizing brain problems in ultrasound mind pics. Timely scientific action is appreciably aided through the early and accurate analysis of mind abnormalities in ultrasound pics. In mild of these findings, it's far obvious that accurate categorization techniques are vital for improving patient consequences through facilitating in advance diagnoses. It is hard to stumble on abnormalities in the brain using ultrasound pics because of historical past noise, artifacts, and the need for correct localization. This paper attracts attention to these problems and proposes a strategy for overcoming them through a Signal Processing Network. For better class of brain problems, this studies proposes a unique method that combines function extraction techniques and a Signal Processing Network: Deep learning-based improved function Ultrasound Brain Images (DL-EFUBI). The technique employs state-of-the-art sign processing algorithms to extract distinguishing traits from ultrasound photos, taking into consideration more particular class of abnormalities. The proposed technique shows promise in some of therapeutic programs, such as the analysis of brain tumors, cysts, and other irregularities at an early degree. It can assist scientific practitioners make higher choices, which in flip blessings affected person care and results. Extensive simulation studies, wherein the cautioned method is compared to mounted methods, is used to assess the approach's efficacy. The effects display that the method has the ability to outperform general approaches close to type accuracy, sensitivity, and specificity. © 2023 IEEE.

关键词： Feature extraction

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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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Efficient IRS-Aided Rate Optimizations for Dual Functional Radar and Communications

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IEEE Communications Letters 2024年第1期28卷 39-42页

作者： Sweta, S. Dubey, Ankit Nguyen, Van-Linh Indian Institute of Technology Jammu Department of Electrical Engineering Jammu and Kashmir Jammu181221 India National Chung Cheng University Department of Computer Science and Information Engineering Chiayi62102 Taiwan

This letter presents a novel method to enable beam steering optimization for secure communications and resilience against radio tracking in an intelligent reflecting surfaces-enabled multiple-input multiple-output dual-functional radar and communications system. The approach involves optimizing beam steering by solving two convex sub-problems: i) maximizing secrecy rate and ii) maximizing legitimate receiver rate, solved using Taylor series approximation. Evaluation results demonstrate that optimizing radar and beam steering enhances secure communications while maintaining quality of service. Furthermore, the utilization of IRS effectively prevents passive eavesdropping in both line-of-sight (LOS) and non-LOS scenarios. © 1997-2012 IEEE.

关键词： Convex optimization

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Tracking of Artillery Shell using Optical Flow 23

Tracking of Artillery Shell using Optical Flow

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5th International Conference on Image Processing and Machine Vision, IPMV 2023

作者： Masum, Abdullah Al Reza, Shad Sharmin, Nusrat Rahman, Md. Mahbubur Department of Computer Science and Engineering Military Institute of Science & Technology Dhaka Bangladesh

ISBN: (纸本)9781450397926

The effectiveness of the Artillery shell is measured by its precision of hitting the target. During hitting the target, the intended firing path can be affected by the inherited source as well as external factors like wind, temperature changes, etc. Thus tracking such a path becomes crucial. In this paper, we address the problem of artillery shell tracking with optical flow and adopted the solution for it with sparse and dense optical flow. The outcome of this work can be used in test firing to determine the accuracy of the artillery target. Experimental findings show that the proposed methodology is more effective than the current military artillery unguided shell's impact point locating, even if the shell ended up being an unexplored or blind shell. © 2023 ACM.

关键词： Optical flows

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Optimizing Waste Management through Intelligent Machine Learning Systems: A Comparative Study 14

Optimizing Waste Management through Intelligent Machine Lear...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Shravani, Nandikante Vigneshwari, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

The purpose of this research study is to examine the use of intelligent machine learning systems in the process of optimising waste management practises. Several other approaches, such as Artificial Neural Networks, K-nearest neighbours, Logistic regression, Decision Trees, Support Vector Machine, and Random Forest, are being investigated as potential ways to improve the currently utilised waste management paradigm. On the other hand, with the implementation of algorithms for machine learning, these measures saw a huge improvement. The use of these methods brought in a remarkable increase in classification accuracy, which reached 92.349%, and a recall rate of 91.04%. This significant improvement in performance demonstrates the usefulness of machine learning in the process of optimising waste management. The Random Forest classifier proved to be the strategy that offered the best results after being compared to the other approaches investigated. In particular, the use of this classifier helped to improve the accuracy of estimates for the amount of time required to empty recycling containers. In a nutshell, the purpose of this study article was to demonstrate how effective intelligent machine learning systems can be in improving existing waste management procedures. © 2023 IEEE.

关键词： Neural networks

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A Comprehensive Review of Machine Learning Techniques for Malaria Diagnosis from Blood Samples

A Comprehensive Review of Machine Learning Techniques for Ma...

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2023 IEEE Conference on engineering Informatics, EI 2023

作者： Muhammad Shameem, P. Malarvel, Muthukumaran Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350338522

ISBN: (纸本)9798350338522

This study paper provides an overview of the use of machine learning techniques for malaria diagnosis. The article looks at a variety of machine learning algorithms, such as supervised and unsupervised learning, deep learning techniques, and hybrid approaches, and how they might be used to diagnose malaria. In addition, widely used data sets and preprocessing methods for detecting malaria are discussed, as well as evaluation metrics for determining how well machine learning models work. The paper highlights how machine learning might improve patient outcomes and change the diagnosis of malaria, particularly in areas with limited resources. The paper concludes with a summary of key findings and contributions, limitations, and open research questions for future work. © 2023 IEEE.

关键词： Support vector machines

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Insights into the Emotional Landscape of Bangladeshi Sports Fans: Deep Learning Based Sentiment Analysis of Sports-Related Bengali Comments on Facebook 17

Insights into the Emotional Landscape of Bangladeshi Sports ...

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17th International Conference on Signal-Image technology and Internet-Based Systems, SITIS 2023

作者： Karim, Anwarul Islam, Anik Sharmin, Sadia Bangladesh University of Engineering and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350370911

People around the globe find sports to be one of the most intriguing topics to discuss on Facebook. Through their comments on sports-related posts, mostly about Cricket and Football, they show strong and immediately noticeable feelings. To predict the sentiments and opinions conveyed by users in Bengali, particularly within the sports context, sentiment analysis can play a vital role. So our research study focuses on conducting sentiment analysis on comments obtained from sports-related posts on Facebook pages, aiming to gain valuable insights into the sentiments expressed by the audience. The methodology involves the collection of a comprehensive dataset from the comment sections of many sports-related Facebook posts, followed by applying data pre-processing techniques to eliminate noise and other unnecessary information. Subsequently, a range of machine learning and deep learning models, including Random Forest, Decision Tree, KNN, SVM, MNB, as well as deep learning models such as Bi-LSTM, CNN-LSTM hybrid, MultiLayer Perceptron with Embedding, Bi-LSTM GRU hybrid, and Bangla Bert are employed for comparative analysis of the sentiments expressed in the comments. Based on the results, we discovered that, out of all the models we trained, RBF SVM was the most stable and well-fitting. The findings of this study will provide significant insights into the overall sentiment prevailing in sports-related discussions on Facebook, contributing to a more profound understanding of users' emotional engagement in the sports domain. © 2023 IEEE.

关键词： Sentiment analysis

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A Comprehensive Survey on Metaheuristic Algorithm for Feature Selection Techniques

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Materials Today: Proceedings 2022年 64卷 435-441页

作者： Arun Kumar, R. Vijay Franklin, J. Koppula, Neeraja Department of Information Technology Bannari Amman Institute of Technology Sathyamangalam Erode India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode India Department of Computer Science and Engineering Geethanjali College of Engineering and Technology Hyderabad Hyderabad India

In Healthcare industries, patients’ medical records consist of a greater number of features or attributes related to certain health factors and medical terminologies. In these feature set, many attributes are either redundant one or irrelevant to the medical disease that is going to be dealt with it. These redundant and irrelevant feature sets is not going to enhance the final result at any cost and moreover it out bounds the final prediction. Feature selection is the process of selecting feature sets in a neural model that are related to a specific problem in order to aid in the inclusion of relevant features and the exclusion of irrelevant or redundant features, thereby reducing the amount of noisy data and the size of the entire dataset. In this paper, metaheuristic related techniques are taken into account and summarized about its key feature along with how it works. Also, various Feature selections related research works that have been published in last three years are highlighted with its proposed methodology, benefits and limitations. From this, it is inferred that many open challenges and enhancements are still available in the feature selection problem even though many researchers introduced their novelty in this problem. © 2022

关键词： Feature Selection

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Automated Interview through Online Video Interface

Automated Interview through Online Video Interface

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Joshila Grace, L.K. Asha, P. Shabu, Jany Refonaa, J. Selvan, Mercy Paul Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350333244

Automated Video Interview Interface is a streamlined approach to job interviews that uses AI and video conferencing. This system can evaluate candidates and make hiring decisions by utilizing natural language processing, facial recognition, and machine learning algorithms, dispensing with the need for physical interviews while enhancing the recruitment process efficiency and decision quality. This technology allows organizations to conduct video interviews with candidates remotely, using pre-recorded or live questions, and also provides the added ability to evaluate and score the candidate's responses. Additionally, it offers the added benefit of personalized feedback for candidates and detailed performance reports for employers. In today's fast-paced recruitment environment, managing effective human interaction, recognizing candidates' facial expressions and body language, and resolving technical issues such as network connectivity, camera and microphone access can be challenging. However, this innovative application offers a solution by providing an efficient interface for accurate candidate assessment and valuable insights. The system not only facilitates the creation, analysing, and evaluating a large number of candidates, but also generates performance reports and personalized feedback based on their performance. This research paper provides an overview of the concept, benefits, and limitations of AVII, and discusses the potential impact of this technology on the recruitment process. It delves into the numerous advantages of implementing an Automated Video Interview Interface, as well as addressing potential challenges and the future impact of this technology on the job market. By utilizing this application, candidates can perform in a secure and private environment with efficient responses of candidate's performance. © 2023 IEEE.

关键词： Video conferencing

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Statistical Testing & TOPSIS Analysis of Acute Sinusitis Interventions

Statistical Testing & TOPSIS Analysis of Acute Sinusitis Int...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Pisal, Tejas Kumar, Hemant Kumar, Praveen Gote, Pradanyawant Faculty of Engineering & Technology Department of Computer Science & Design Wardha Maharashtra Sawangi442001 India Faculty of Engineering & Technology Department of Basic Sciences and Humanities Wardha Maharashtra Sawangi442001 India Faculty of Engineering & Technology Department of Computer Science & Medical Engineering Wardha Maharashtra Sawangi442001 India

ISBN: (纸本)9798350361537

Acute sinusitis, often occurring as a consequence of recovering from a cold, presents a significant health concern due to its impact on the interconnected hollow spaces located behind the cheekbones, forehead, and nose. These sinus passages play a crucial role in the respiratory process by facilitating the smooth flow of air towards the lungs. Given the discomfort and potential complications associated with acute sinusitis, there is a pressing need for effective pharmaceutical interventions. This paper embarks on a comprehensive analysis of such interventions, leveraging real-world data subjected to rigorous examination through the Kruskal-Wallis statistical testing and the TOPSIS decision-making method. Our study is dedicated to discerning the most effective drug for acute sinusitis, considering a spectrum of factors including efficacy, ease of use, user satisfaction, and cost-effectiveness. By synthesizing empirical evidence and statistical analyses, we aim to provide valuable insights that can inform clinical decision-making and optimize patient care in the management of acute sinusitis. This research holds promise in guiding healthcare providers towards evidence-based prescribing practices, enhancing treatment outcomes, and ultimately improving the quality of life for individuals suffering from acute sinusitis. © 2024 IEEE.

关键词： Decision making

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