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From Pixels to Diagnosis: Convolutional Neural Networks in Tuberculosis Screening 6

From Pixels to Diagnosis: Convolutional Neural Networks in T...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Zihan, Tanvir Mahtab Zami, Abduz Ahmed, Mohiuddin Islam, Rakibul Ahmed, Fahim Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh Uiu Dept. of Cse Dhaka Bangladesh

ISBN: (纸本)9798350385779

Tuberculosis (TB) is a chronic lung disease that constitutes one of the top 10 global causes of death. Timely and precise identification of TB is critical, as untreated cases can pose life-threatening risks. Traditional radiographic interpretation by trained professionals is subjective, time-consuming, and prone to human error. This study proposes an automated deep learning-based approach for TB detection from chest X-ray images, based on EfficientNet-B0 architecture. The Convolutional Neural Network (CNN) extracts discriminative features, allowing it to differentiate between normal and abnormal lung patterns indicative of TB. Our choice of the EfficientNet-B0 architecture played a pivotal role in the success of our TB detection model. Training and evaluation were conducted on three distinct datasets: Tuberculosis (TB) Chest X-ray Database, Shenzhen Hospital CXR Set, and Montgomery County CXR Set. The key breakthrough is attaining an accuracy of 99.29% on the Tuberculosis (TB) Chest X-ray Database and 98.50% on the combined dataset. This study presents a promising approach for automated TB screening using readily available chest X-ray images, potentially improving diagnostic accuracy and efficiency, especially in resource-limited settings. We compared our scores with those of other works and found that ours significantly outperforms them. © 2024 IEEE.

关键词： Diagnosis

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Identifying Hacking Failures in the IaaS, PaaS, and SaaS Networks to Secure Cloud Applications 6

Identifying Hacking Failures in the IaaS, PaaS, and SaaS Net...

引用

6th Parul International Conference on engineering and Technology, PICET 2024

作者： Bhavsar, Rakesh Thakar, Vishvjit Dept. of Computer Science Engineering School of Engineering Indrashil University Gujarat Mahesana India

ISBN: (纸本)9798350369748

From small businesses to major corporations, Cloud computing is quickly taking over as the preferred platform. due to the obligation to guarantee that cloud apps are safe and can shield data from unauthorized attackers. According to the joint responsibility model of Cloud Security, organizations who utilize these services are also responsible for maintaining their degree of security while service providers are responsible for making sure that their services are safe. A shared responsibility Paradigm for identity and access management transfers part of the client's responsibility for application security. Setting up procedures for identity management and access control as well as carrying out ethical hacking and penetration testing are required to guarantee the greatest degree of data security. Since Cloud Security has developed into a shared duty between the user and the service provider, clients are now required to take accountability for the security of their applications in the cloud. IaaS and PaaS services are the major beneficiaries of this. Advanced security procedures like ethical hacking and penetration testing are necessary to provide a safe virtual environment. Organizations will be able to avoid harmful assaults and data leaks thanks to these efforts. Raising risk visibility, identifying vulnerabilities, minimizing risks, and gathering crucial telemetry data are the greatest ways to enhance security. The primary goal of this study article is to assess the techniques for designing a framework or checklist to identify associated risks & vulnerabilities while at the same time attacking cloud-based applications. As a consequence, malicious actors will be kept out of cloud-based programs. © 2024 IEEE.

关键词： Cloud security

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Automated Leukemia Diagnosis from Microscopic Blood Smear Images Using Adapted Inception V3

Automated Leukemia Diagnosis from Microscopic Blood Smear Im...

引用

2024 International Conference on Big Data Analytics in Bioinformatics, DABCon 2024

作者： Pujitha, M Vani Srikantam, Srinija Sahithi, Ch Timmasarti, Akhila Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350378047

Leukemia, a cancer characterized by the rapid overproduction of abnormal blood cells, presents notable diagnostic and treatment challenges. Identifying leukemia subtypes promptly and accurately is vital for devising effective treatments and improving patient outcomes. This study proposes an advanced Inception V3 model optimized for the automatic identification of leukemia from microscopic images of blood smears. By integrating cutting-edge deep learning methods and a customized model architecture, this system differentiates between Acute Lymphoblastic Leukemia (ALL), Acute Myeloid Leukemia (AML), Chronic Lymphocytic Leukemia (CLL), Chronic Myeloid Leukemia (CML), and healthy samples. The model benefits from preprocessing techniques like image resizing, normalization, and augmentation to enhance its performance. Through transfer learning, the Inception V3 model is fine-tuned on a rich dataset covering various leukemia subtypes. Experimental results demonstrate the model's high effectiveness, achieving 100% accuracy during training and 99.72% accuracy in testing, proving the system's reliability for automated leukemia detection. © 2024 IEEE.

关键词： Lung cancer

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A 4x4 MIMO Antenna Array for the Wi-Fi7Access Point on a Metal Wall Application

A 4x4 MIMO Antenna Array for the Wi-Fi7Access Point on a Met...

引用

2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Su, Hsin-Lung Zhan, You-Sheng Dept. of Computer and Communication National Pingtung University Taiwan Dept. of Computer Science and Information Engineering National Pingtung University Taiwan

ISBN: (纸本)9798350364774

A 4×4 CPW MIMO Antenna array with an artificial magnetic conductor (AMC) reflector is presented for Wi-Fi7 access point on a metal wall application in the study. The 10-dB impedance of the integrated MIMO antenna array covers the Wi-Fi 7 operating bands, and the transmission coefficients are under -15 dB in the whole Wi-Fi7 bands. This proposed integrated MIMO antenna is low profile and has good total radiation efficiency (over 60%). The height of the integrated MIMO antenna array is 6.325 mm. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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Implementation of human-following robot system applying virtual delay to posture of a leader 3

Implementation of human-following robot system applying virt...

引用

3rd International Conference on Image Processing and Robotics, ICIPRoB 2024

作者： Takemori, Fumiaki Tottori University Faculty of Engineering Dept. of Electrical Engineering and Computer Science Tottori Japan

ISBN: (纸本)9798350374766

This paper develops an implementation of a measurement and control system in which a vehicle follows its predecessor while maintaining a certain distance. First, we construct a model that virtually delays the reference coordinates of the leading target backwards, and propose a control law that follows the steering angle of the following vehicle to that virtual delay point. Next, a range sensor is used to detect the position and orientation of the leader, and the position and orientation is estimated using a template matching method based on the person's body contour point group measured by the sensor. Finally, the effectiveness (including robustness) of the proposed system is demonstrated in some simulations and human tracking experiments. © 2024 IEEE.

关键词： Template matching

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Hybrid ConvLSTM with Attention for Precise Driver Behavior Classification 6

Hybrid ConvLSTM with Attention for Precise Driver Behavior C...

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6th IEEE International Conference on Artificial Intelligence and Knowledge engineering, AIKE 2023

作者： Mustafa, Ghulam Cho, Seong-Je Hwang, Youngsup Dept. of Computer Engineering Sun Moon University Asan-si Korea Republic of Dept. of Software Science Dankook University Yongin Korea Republic of

ISBN: (纸本)9798350331288

Driving behavior classification plays an important role in many real-world applications, including traffic accident prevention, driver safety, usage-based insurance, and optimizing ridesharing services. In this research, we introduce a novel approach called Hybrid ConvLSTM with Attention for Driver Behavior Classification (HCLA-DBC), designed to achieve precise and reliable driver behavior classification. Our main focus is to distinguish three fundamental driving behavior classes: normal, drowsy, and aggressive, which play a vital role in supporting advanced driver-assistance systems (ADAS). To evaluate the performance of our proposed models, we conducted experiments using the publicly available UAH-DriveSet dataset. The evaluation comprised six distinct models, encompassing two machine learning models (SVM and Gradient Boosting) and four deep learning models (CNN, MLP, LSTM, and HCLADBC). Our HCLA-DBC model outperformed all other models and previous results on the same data. On the test data, it achieved an accuracy of 94.12%, precision of 94.24%, recall score of 94.12%, F1-score of 94.12%, and a macro-average ROC AUC of 98.83. These exceptional outcomes underscore the efficacy of our hybridized approach and its potential to significantly enhance driver behavior classification. The HCLA-DBC model not only promises new possibilities for driver behavior analysis but also paves the way for elevated ADAS capabilities and safer driving experiences. © 2023 IEEE.

关键词： Support vector machines

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Integration of Swarm Intelligence with KNN for Optimal Nearest Neighbors Value Prediction 9

Integration of Swarm Intelligence with KNN for Optimal Neare...

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9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Anchalia, Aniketh Kakati, Anirudh Patil, Ambika Bhavana, B.N. Paudel, Ankit Chaudhari, Shilpa Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350334395

K-Nearest Neighbors (KNN), a simple and widely used algorithm, is extremely valuable in the field of machine learning models. Finding an optimal value of the nearest neighbor parameter in the KNN algorithm has been a problem that has existed for quite some time. In recent years, nature inspired algorithms have been widely emerging, with scope of being utilized in various engineering problems. This work proposes integrating a swarm intelligence optimization technique, the Sparrow Search Algorithm (SSA), for optimizing the process of finding K. The proposed hybrid model has been tested against four datasets of varying sizes and the results depict improvement in efficiency. The improvement is much larger as the size of the dataset increases, with around a 99% improvement in efficiency for the largest dataset tested on. KNN is also used to preprocess two of the datasets which have missing values, which result in improved accuracies of prediction. © 2023 IEEE.

关键词： Swarm intelligence

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Advancements in User Security: Enhancing Usability with Graphical Password Authentication 2

Advancements in User Security: Enhancing Usability with Grap...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Harisha Naik, Sandhya Ramesh Vasudeva, Shettigar Sarvani Shrilakshmi, K. Kothwal, Vaishnavi Sahyadri College of Engineering & Management Dept. of Computer Science & Engineering Mangalore Karnataka India

ISBN: (纸本)9798350327533

Text-based password authentication is a common method used to verify the identity of users who are trying to access a secure system or service. In order to use this authentication method, the user must input a password or other secret phrase that is then compared to a server-side copy of the same password. Access is given if the password typed matches the one saved. Graphical password authentication is a type of user authentication that involves using images or visual elements instead of alphanumeric characters to verify the identity of the user. Unlike traditional text-based passwords, graphical passwords offer an intuitive and user-friendly way of authentication, as they rely on the user's ability to remember pictures, shapes, and patterns. This technology has been developed to address the limitations of traditional text-based passwords, such as the difficulty of creating and remembering complex passwords, and the vulnerability to brute-force attacks. Compared to conventional text-based passwords, graphical password authentication has a number of benefits, including better usability and higher security. © 2024 IEEE.

关键词： Authentication graphical passwords security

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NextGen Dynamic Video Generator using AI 9

NextGen Dynamic Video Generator using AI

引用

9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Anitha, R. Kishore, N. Vijay Anand, M. Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384208

In an era marked by remarkable technological advancements, the way we create and share information has undergone a profound transformation. This paradigm shift is epitomized by the NextGen Dynamic Video Generator using AI, a cutting-edge tool that seamlessly integrates artificial intelligence with content creation. What sets this tool apart is the customizability, which allows the users to fine-tune every aspect of the tutorials. Users could tune creative elements like humor, the dept. of explanation, character appearance, and voice to tailor tutorials to precise specifications. The core functionality of the tool revolves around script generation, the Cohere's language, laying the groundwork for the tutorial's content. Furthermore, seamless integration with Edge TTS ensures that the generated scripts are delivered with utmost clarity and engagement, enhancing the overall learning experience. Character animation is powered by SadTalker, adding a dynamic and captivating dimension to these tutorials. This animated character serves as a relatable guide, facilitating a deeper connection between the content and the audience. The tool also seamlessly integrates relevant and eye-catching images from Google, which are incorporated into the presentation slides. The workflow is a well-orchestrated process involving script generation, audio dialogue creation, image retrieval, video generation, and the seamless fusion of character animations and slides. The resulting video tutorials are not only comprehensive but also engaging and ready to be shared as valuable educational resources. © 2023 IEEE.

关键词： Image retrieval

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ML Assisted Web Application Firewall 12

ML Assisted Web Application Firewall

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Kalariya, Pranav Jethva, Manthan Alginahi, Yasser University of Windsor Electrical and Computer Engineering Dept. Windsor Canada Computer Science Dept. Adrian College MI United States

ISBN: (纸本)9798350330366

This study presents the development and implementation of a sophisticated Web Application Firewall (WAF) empowered by machine learning techniques to bolster cybersecurity measures. Traditional WAFs primarily rely on rule-based systems, which may struggle to adapt to the evolving nature of web-based threats. In contrast, our proposed solution leverages machine learning algorithms to dynamically analyze and respond to emerging cyber threats, providing a more proactive and adaptive defense mechanism. The core functionality of the system involves the continuous monitoring of incoming web traffic, extracting relevant features, and utilizing a machine learning model to classify the traffic as either benign or malicious. The model is trained on historical data to recognize patterns and behaviors indicative of various cyber threats, including SQL injection, cross-site scripting, and other common attack vectors. Through this learning process, the system becomes adept.at discerning malicious activities and adapting its defense strategies accordingly. The proposed model helps achieve higher precision in identifying the threat requests from normal requests. © 2024 IEEE.

关键词： Learning algorithms

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